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ORCID36th NeurIPS 2022: New Orleans, LA, USA
- Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, A. Oh:
Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022. 2022, ISBN 9781713871088 - Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Yanghe Feng, Guihai Chen:
Federated Submodel Optimization for Hot and Cold Data Features. - Xingyu Zhou, Bo Ji:
On Kernelized Multi-Armed Bandits with Constraints. - Seon-Ho Lee, Nyeong-Ho Shin, Chang-Su Kim:
Geometric Order Learning for Rank Estimation. - Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani:
Structured Recognition for Generative Models with Explaining Away. - Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu:
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search. - Cian Naik, Judith Rousseau, Trevor Campbell:
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. - Steven Stalder, Nathanaël Perraudin, Radhakrishna Achanta, Fernando Pérez-Cruz, Michele Volpi:
What You See is What You Classify: Black Box Attributions. - Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola:
Adaptive Interest for Emphatic Reinforcement Learning. - Dongze Lian, Daquan Zhou, Jiashi Feng, Xinchao Wang:
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning. - Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, Cordelia Schmid:
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models. - Yinglun Zhu, Robert Nowak:
Active Learning with Neural Networks: Insights from Nonparametric Statistics. - Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, YingLei Wang, Xuhui Huang, Zhe Ma:
IM-Loss: Information Maximization Loss for Spiking Neural Networks. - Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Jonathan D. Cohen, Nathaniel D. Daw, Karthik Narasimhan, Tom Griffiths:
Using natural language and program abstractions to instill human inductive biases in machines. - Ruibo Liu, Chenyan Jia, Ge Zhang, Ziyu Zhuang, Tony X. Liu, Soroush Vosoughi:
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits. - Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon:
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. - Mathieu Even, Laurent Massoulié, Kevin Scaman:
On Sample Optimality in Personalized Collaborative and Federated Learning. - Wei-Cheng Tseng, Tsun-Hsuan Johnson Wang, Yen-Chen Lin, Phillip Isola:
Offline Multi-Agent Reinforcement Learning with Knowledge Distillation. - Shuoguang Yang, Xuezhou Zhang, Mengdi Wang:
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. - Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
Conditional Meta-Learning of Linear Representations. - Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht:
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources. - Shichong Peng, Seyed Alireza Moazenipourasil, Ke Li:
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis. - Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud:
Active Ranking without Strong Stochastic Transitivity. - Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani:
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression. - Yiting Chen, Qibing Ren, Junchi Yan:
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain. - Zhun Zhong, Yuyang Zhao, Gim Hee Lee, Nicu Sebe:
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation. - Lue Fan, Feng Wang, Naiyan Wang, Zhaoxiang Zhang:
Fully Sparse 3D Object Detection. - Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein:
Diffusion Visual Counterfactual Explanations. - Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, Luc Van Gool:
Recurrent Video Restoration Transformer with Guided Deformable Attention. - Boxiang Wang, Archer Y. Yang:
A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction. - Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. - Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. - Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding:
Coresets for Relational Data and The Applications. - Kaiyang Guo, Yunfeng Shao, Yanhui Geng:
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief. - Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han:
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. - Florentin Guth, Simon Coste, Valentin De Bortoli, Stéphane Mallat:
Wavelet Score-Based Generative Modeling. - Chen Liu, Ziqi Zhao, Sabine Süsstrunk, Mathieu Salzmann:
Robust Binary Models by Pruning Randomly-initialized Networks. - Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux:
Why do tree-based models still outperform deep learning on typical tabular data? - Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama:
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. - Vivek F. Farias, Andrew A. Li, Tianyi Peng, Andrew Zheng:
Markovian Interference in Experiments. - Paul Rolland, Luca Viano, Norman Schürhoff, Boris Nikolov, Volkan Cevher:
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning. - Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell:
Parallel Tempering With a Variational Reference. - Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. - Rui Miao, Zhengling Qi, Xiaoke Zhang:
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models. - Chaofei Wang, Qisen Yang, Rui Huang, Shiji Song, Gao Huang:
Efficient Knowledge Distillation from Model Checkpoints. - Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, Suhang Wang:
Decoupled Self-supervised Learning for Graphs. - Lujun Li, Zhe Jin:
Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer. - Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, Shuiwang Ji:
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs. - Kaizhi Zheng, Xiaotong Chen, Odest Chadwicke Jenkins, Xin Eric Wang:
VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation. - Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu:
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. - Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán:
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. - Jiayuan Ye, Reza Shokri:
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence). - Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson:
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. - Luca Beurer-Kellner, Martin T. Vechev, Laurent Vanbever, Petar Velickovic:
Learning to Configure Computer Networks with Neural Algorithmic Reasoning. - Mingze Wang, Chao Ma:
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks. - Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee:
On Divergence Measures for Bayesian Pseudocoresets. - Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. - Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Multi-Class $H$-Consistency Bounds. - Sameera Ramasinghe, Lachlan E. MacDonald, Simon Lucey:
On the Frequency-bias of Coordinate-MLPs. - Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. - Siyu Jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Honghui Shi:
Mask Matching Transformer for Few-Shot Segmentation. - Ilai Bistritz, Nicholas Bambos:
Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits. - Wei Dong, Yuting Liang, Ke Yi:
Differentially Private Covariance Revisited. - Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen:
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. - Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. - Wenjian Huang, Hao Wang, Jiahao Xia, Chengyan Wang, Jianguo Zhang:
Density-driven Regularization for Out-of-distribution Detection. - Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian J. Theis, Niki Kilbertus:
Sparsity in Continuous-Depth Neural Networks. - Bo-Wei Huang, Keng-Te Liao, Chang-Sheng Kao, Shou-De Lin:
Environment Diversification with Multi-head Neural Network for Invariant Learning. - Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu:
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. - Yuxin Zhang, Mingbao Lin, Zhihang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, Rongrong Ji:
Learning Best Combination for Efficient N: M Sparsity. - Yue Xing, Qifan Song, Guang Cheng:
Why Do Artificially Generated Data Help Adversarial Robustness. - Hongrui Cai, Wanquan Feng, Xuetao Feng, Yan Wang, Juyong Zhang:
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera. - Jiayang Ren, Kaixun Hua, Yankai Cao:
Global Optimal K-Medoids Clustering of One Million Samples. - Shibo Li, Jeff M. Phillips, Xin Yu, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Active Learning with Budget Constraints. - Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, Junmo Kim:
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training. - Cong Guan, Feng Chen, Lei Yuan, Chenghe Wang, Hao Yin, Zongzhang Zhang, Yang Yu:
Efficient Multi-agent Communication via Self-supervised Information Aggregation. - Ramansh Sharma, Varun Shankar:
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations. - Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-François Chamberland:
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning. - Yeoneung Kim, Insoon Yang, Kwang-Sung Jun:
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs. - Zhuoqing Song, Weijian Li, Kexin Jin, Lei Shi, Ming Yan, Wotao Yin, Kun Yuan:
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate. - Biru Zhu, Yujia Qin, Ganqu Cui, Yangyi Chen, Weilin Zhao, Chong Fu, Yangdong Deng, Zhiyuan Liu, Jingang Wang, Wei Wu, Maosong Sun, Ming Gu:
Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models. - Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, Xinchao Wang:
Dataset Distillation via Factorization. - Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul M. Zimmerman:
Adaptive Sampling for Discovery. - Lixin Zou, Haitao Mao, Xiaokai Chu, Jiliang Tang, Wenwen Ye, Shuaiqiang Wang, Dawei Yin:
A Large Scale Search Dataset for Unbiased Learning to Rank. - Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, Shi-Min Hu:
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation. - Tao Yu, Yichi Zhang, Zhiru Zhang, Christopher De Sa:
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning. - Qin Ding, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. - Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, Preetum Nakkiran:
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. - Yuanhao Ban, Yinpeng Dong:
Pre-trained Adversarial Perturbations. - Jinkun Cao, Ruiqian Nai, Qing Yang, Jialei Huang, Yang Gao:
An Empirical Study on Disentanglement of Negative-free Contrastive Learning. - Mo Tiwari, Ryan Kang, Jaeyong Lee, Chris Piech, Ilan Shomorony, Sebastian Thrun, Martin J. Zhang:
MABSplit: Faster Forest Training Using Multi-Armed Bandits. - Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. - Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien:
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints. - Rishi Saket:
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions. - Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov, Beren Millidge, Yuhang Song, Thomas Lukasiewicz:
Predictive Coding beyond Gaussian Distributions. - Nived Rajaraman, Devvrit, Pranjal Awasthi:
Semi-supervised Active Linear Regression. - Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang:
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. - Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe:
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses. - Cristopher Salvi, Maud Lemercier, Andris Gerasimovics:
Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics. - Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, Novi Quadrianto:
Okapi: Generalising Better by Making Statistical Matches Match. - Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup:
Revisiting Heterophily For Graph Neural Networks. - Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei Ye, Shikun Zhang, Tao Qin, Tie-Yan Liu:
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation. - Mathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux, Florian Strub:
Emergent Communication: Generalization and Overfitting in Lewis Games. - Haoli Bai, Lu Hou, Lifeng Shang, Xin Jiang, Irwin King, Michael R. Lyu:
Towards Efficient Post-training Quantization of Pre-trained Language Models. - Shubhanshu Mishra, Aman Saini, Raheleh Makki, Sneha Mehta, Aria Haghighi, Ali Mollahosseini:
TweetNERD - End to End Entity Linking Benchmark for Tweets. - Sung Woo Park, Hyomin Kim, Kyungjae Lee, Junseok Kwon:
Riemannian Neural SDE: Learning Stochastic Representations on Manifolds. - Marta R. Costa-jussà, Christine Basta, Oriol Domingo, André Rubungo:
OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations. - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. - Tingliang Feng, Wei Feng, Weiqi Li, Di Lin:
Cross-Image Context for Single Image Inpainting. - Sravanti Addepalli, Samyak Jain, Venkatesh Babu R.:
Efficient and Effective Augmentation Strategy for Adversarial Training. - Tongda Xu, Yan Wang, Dailan He, Chenjian Gao, Han Gao, Kunzan Liu, Hongwei Qin:
Multi-Sample Training for Neural Image Compression. - Yifan Yang, Yang Liu, Parinaz Naghizadeh:
Adaptive Data Debiasing through Bounded Exploration. - Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus:
Learning to Navigate Wikipedia by Taking Random Walks. - David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? - Qi Lyu, Xiao Fu:
Provable Subspace Identification Under Post-Nonlinear Mixtures. - Wenqi Yang, Guanying Chen, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K. Wong:
S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint. - Arindam Ghosh, Thomas Schaaf, Matthew R. Gormley:
AdaFocal: Calibration-aware Adaptive Focal Loss. - Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert:
PDEBench: An Extensive Benchmark for Scientific Machine Learning. - Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou:
Learning Robust Dynamics through Variational Sparse Gating. - Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? - Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm:
Maximizing Revenue under Market Shrinkage and Market Uncertainty. - Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
General Cutting Planes for Bound-Propagation-Based Neural Network Verification. - Aakash Kaku, Kangning Liu, Avinash Parnandi, Haresh Rengaraj Rajamohan, Kannan Venkataramanan, Anita Venkatesan, Audre Wirtanen, Natasha Pandit, Heidi M. Schambra, Carlos Fernandez-Granda:
StrokeRehab: A Benchmark Dataset for Sub-second Action Identification. - Yossi Azar, Amos Fiat, Federico Fusco:
An $\alpha$-regret analysis of Adversarial Bilateral Trade. - Mingyu Yang, Jian Zhao, Xunhan Hu, Wengang Zhou, Jiangcheng Zhu, Houqiang Li:
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning. - Jiafei Lyu, Xiaoteng Ma, Xiu Li, Zongqing Lu:
Mildly Conservative Q-Learning for Offline Reinforcement Learning. - Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski:
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. - Hongwei Jin, Zishun Yu, Xinhua Zhang:
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats. - Coby Penso, Idan Achituve, Ethan Fetaya:
Functional Ensemble Distillation. - Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. - Wenxiao Wang, Alexander Levine, Soheil Feizi:
Lethal Dose Conjecture on Data Poisoning. - Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Online Decision Mediation. - Zezhong Xu, Wen Zhang, Peng Ye, Hui Chen, Huajun Chen:
Neural-Symbolic Entangled Framework for Complex Query Answering. - Tairan He, Yuge Zhang, Kan Ren, Minghuan Liu, Che Wang, Weinan Zhang, Yuqing Yang, Dongsheng Li:
Reinforcement Learning with Automated Auxiliary Loss Search. - Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo:
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. - Klim Zaporojets, Lucie-Aimée Kaffee, Johannes Deleu, Thomas Demeester, Chris Develder, Isabelle Augenstein:
TempEL: Linking Dynamically Evolving and Newly Emerging Entities. - Pingyi Hu, Zihan Wang, Ruoxi Sun, Hu Wang, Minhui Xue:
M$^4$I: Multi-modal Models Membership Inference. - Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Best of Both Worlds Model Selection. - Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi:
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. - Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang:
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. - Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games. - Minsu Kim, Junyoung Park, Jinkyoo Park:
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization. - Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel:
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. - Yiqun Wang, Ivan Skorokhodov, Peter Wonka:
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details. - Lucas Monteiro Paes, Carol Xuan Long, Berk Ustun, Flávio P. Calmon:
On the Epistemic Limits of Personalized Prediction. - Zeyu Yang, Jiaqi Chen, Zhenwei Miao, Wei Li, Xiatian Zhu, Li Zhang:
DeepInteraction: 3D Object Detection via Modality Interaction. - Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Deep Differentiable Logic Gate Networks. - Parth Thaker, Mohit Malu, Nikhil Rao, Gautam Dasarathy:
Maximizing and Satisficing in Multi-armed Bandits with Graph Information. - Yunsong Zhou, Quan Liu, Hongzi Zhu, Yunzhe Li, Shan Chang, Minyi Guo:
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation. - Harikrishnan N. B., Aditi Kathpalia, Nithin Nagaraj:
Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning. - Shurui Gui, Xiner Li, Limei Wang, Shuiwang Ji:
GOOD: A Graph Out-of-Distribution Benchmark. - Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar:
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. - Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding, Bo Du, Anwar Hithnawi, Bo Li, Ce Zhang:
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? - Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun:
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. - Anupam Gupta, Debmalya Panigrahi, Bernardo Subercaseaux, Kevin Sun:
Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions. - Laurynas Karazija, Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi:
Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns. - Ashwinkumar Badanidiyuru Varadaraja, Zhe Feng, Tianxi Li, Haifeng Xu:
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards. - Jiaxing Huang, Kaiwen Cui, Dayan Guan, Aoran Xiao, Fangneng Zhan, Shijian Lu, Shengcai Liao, Eric P. Xing:
Masked Generative Adversarial Networks are Data-Efficient Generation Learners. - Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran:
What You See is What You Get: Principled Deep Learning via Distributional Generalization. - Hanxu Zhou, Qixuan Zhou, Tao Luo, Yaoyu Zhang, Zhi-Qin John Xu:
Towards Understanding the Condensation of Neural Networks at Initial Training. - Chenxin An, Jiangtao Feng, Kai Lv, Lingpeng Kong, Xipeng Qiu, Xuanjing Huang:
CoNT: Contrastive Neural Text Generation. - Yifei Zhou, Renyu Li, Hayden Housen, Ser Nam Lim:
GAPX: Generalized Autoregressive Paraphrase-Identification X. - Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller:
Scalable Infomin Learning. - Tailin Wu, Takashi Maruyama, Jure Leskovec:
Learning to Accelerate Partial Differential Equations via Latent Global Evolution. - Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel S. Brown, Ken Goldberg:
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations. - Nicolas Keriven:
Not too little, not too much: a theoretical analysis of graph (over)smoothing. - Kyle Luther, H. Sebastian Seung:
Kernel similarity matching with Hebbian networks. - Haoran Wei, Ping Guo, Yangguang Zhu, Chenglong Liu, Peng Wang:
HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process. - Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta:
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. - Tianqi Wei, Rana Alkhoury Maroun, Qinghai Guo, Barbara Webb:
DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes. - Benoit Dherin, Michael Munn, Mihaela Rosca, David Barrett:
Why neural networks find simple solutions: The many regularizers of geometric complexity. - Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh:
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. - Sander Beckers, Hana Chockler, Joseph Y. Halpern:
A Causal Analysis of Harm. - Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer:
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. - Arman Zharmagambetov, Miguel Á. Carreira-Perpiñán:
Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization. - Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet:
Riemannian Score-Based Generative Modelling. - Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy P. Lillicrap, Gregory Wayne:
Intra-agent speech permits zero-shot task acquisition. - Reda Chhaibi, Tariq Daouda, Ezechiel Kahn:
Free Probability for predicting the performance of feed-forward fully connected neural networks. - Shilong Bao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang:
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm. - Robert Meier, Asier Mujika:
Open-Ended Reinforcement Learning with Neural Reward Functions. - Ibrahim M. Alabdulmohsin, Jessica Schrouff, Sanmi Koyejo:
A Reduction to Binary Approach for Debiasing Multiclass Datasets. - Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, Ming Tang:
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks. - Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, Ashwin Kalyan:
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering. - Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman:
Few-Shot Audio-Visual Learning of Environment Acoustics. - Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. - Xiangzhe Kong, Wenbing Huang, Zhixing Tan, Yang Liu:
Molecule Generation by Principal Subgraph Mining and Assembling. - Zekun Hao, Arun Mallya, Serge J. Belongie, Ming-Yu Liu:
Implicit Neural Representations with Levels-of-Experts. - Zhao-Heng Yin, Weirui Ye, Qifeng Chen, Yang Gao:
Planning for Sample Efficient Imitation Learning. - Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. - Haonan Yu, Wei Xu, Haichao Zhang:
Towards Safe Reinforcement Learning with a Safety Editor Policy. - Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun:
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty. - Kate Sanders, Reno Kriz, Anqi Liu, Benjamin Van Durme:
Ambiguous Images With Human Judgments for Robust Visual Event Classification. - Zhiyu Mou, Yusen Huo, Rongquan Bai, Mingzhou Xie, Chuan Yu, Jian Xu, Bo Zheng:
Sustainable Online Reinforcement Learning for Auto-bidding. - Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai:
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs. - Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. - Zhijie Deng, Feng Zhou, Jun Zhu:
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning. - Emmanuel Abbe, Samy Bengio, Elisabetta Cornacchia, Jon M. Kleinberg, Aryo Lotfi, Maithra Raghu, Chiyuan Zhang:
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures. - Loay Mualem, Moran Feldman:
Using Partial Monotonicity in Submodular Maximization. - Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai:
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments. - Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, Aaron C. Courville:
Riemannian Diffusion Models. - Andy Shih, Dorsa Sadigh, Stefano Ermon:
Training and Inference on Any-Order Autoregressive Models the Right Way. - Shinichi Hemmi, Taihei Oki, Shinsaku Sakaue, Kaito Fujii, Satoru Iwata:
Lazy and Fast Greedy MAP Inference for Determinantal Point Process. - Sejun Park, Umut Simsekli, Murat A. Erdogdu:
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers. - Ying Jin, Jiaqi Wang, Dahua Lin:
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant. - Michael Lindon, Alan Malek:
Anytime-Valid Inference For Multinomial Count Data. - Julien Colin, Thomas Fel, Rémi Cadène, Thomas Serre:
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. - Eric Nguyen, Karan Goel, Albert Gu, Gordon W. Downs, Preey Shah, Tri Dao, Stephen Baccus, Christopher Ré:
S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces. - Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander J. Ratner:
Understanding Programmatic Weak Supervision via Source-aware Influence Function. - Guohao Shen, Yuling Jiao, Yuanyuan Lin, Jian Huang:
Approximation with CNNs in Sobolev Space: with Applications to Classification. - Shinsaku Sakaue, Taihei Oki:
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search. - Zifeng Wang, Jimeng Sun:
TransTab: Learning Transferable Tabular Transformers Across Tables. - Weixia Zhang, Dingquan Li, Xiongkuo Min, Guangtao Zhai, Guodong Guo, Xiaokang Yang, Kede Ma:
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop. - Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang:
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity. - Ching-Yao Chuang, Stefanie Jegelka:
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks. - Yu Shen, Yupeng Lu, Yang Li, Yaofeng Tu, Wentao Zhang, Bin Cui:
DivBO: Diversity-aware CASH for Ensemble Learning. - Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, Jian Pei:
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum. - Kha Pham, Hung Le, Man Ngo, Truyen Tran:
Functional Indirection Neural Estimator for Better Out-of-distribution Generalization. - Qingsong Liu, Weihang Xu, Siwei Wang, Zhixuan Fang:
Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness. - Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying:
Will Bilevel Optimizers Benefit from Loops. - Dan Zhao:
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks. - Mosam Dabhi, Chaoyang Wang, Tim Clifford, László A. Jeni, Ian R. Fasel, Simon Lucey:
MBW: Multi-view Bootstrapping in the Wild. - Yifan Feng, Yuxuan Tang:
On A Mallows-type Model For (Ranked) Choices. - Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. - Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, Jian Li:
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation. - Qizhao Chen, Vasilis Syrgkanis, Morgane Austern:
Debiased Machine Learning without Sample-Splitting for Stable Estimators. - Sharan Vaswani, Lin Yang, Csaba Szepesvári:
Near-Optimal Sample Complexity Bounds for Constrained MDPs. - Ron Amit, Baruch Epstein, Shay Moran, Ron Meir:
Integral Probability Metrics PAC-Bayes Bounds. - Andrea Zanette, Martin J. Wainwright:
Bellman Residual Orthogonalization for Offline Reinforcement Learning. - Tongyang Li, Ruizhe Zhang:
Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits. - Andrew F. Luo, Yilun Du, Michael J. Tarr, Josh Tenenbaum, Antonio Torralba, Chuang Gan:
Learning Neural Acoustic Fields. - Giulia Bernardini, Alexander Lindermayr, Alberto Marchetti-Spaccamela, Nicole Megow, Leen Stougie, Michelle Sweering:
A Universal Error Measure for Input Predictions Applied to Online Graph Problems. - Junzhe Zhang, Elias Bareinboim:
Online Reinforcement Learning for Mixed Policy Scopes. - Seungeon Lee, Xiting Wang, Sungwon Han, Xiaoyuan Yi, Xing Xie, Meeyoung Cha:
Self-explaining deep models with logic rule reasoning. - Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He:
XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient. - Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu:
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. - Devvrit, Aditya Sinha, Inderjit S. Dhillon, Prateek Jain:
S3GC: Scalable Self-Supervised Graph Clustering. - Benjamin Kurt Miller, Christoph Weniger, Patrick Forré:
Contrastive Neural Ratio Estimation. - Hong Jun Jeon, Benjamin Van Roy:
An Information-Theoretic Framework for Deep Learning. - Ioannis Anagnostides, Gabriele Farina, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Tuomas Sandholm:
Uncoupled Learning Dynamics with O(log T) Swap Regret in Multiplayer Games. - Lan-Zhe Guo, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yufeng Li:
Robust Semi-Supervised Learning when Not All Classes have Labels. - Hui Lu, Mia Chiquier, Carl Vondrick:
Private Multiparty Perception for Navigation. - Long-Kai Huang, Ying Wei:
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization. - Baoxiong Jia, Ting Lei, Song-Chun Zhu, Siyuan Huang:
EgoTaskQA: Understanding Human Tasks in Egocentric Videos. - Huaxiu Yao, Yiping Wang, Linjun Zhang, James Y. Zou, Chelsea Finn:
C-Mixup: Improving Generalization in Regression. - Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui, Mickaël Leclercq, Arnaud Droit, Frédéric Precioso:
Generalised Mutual Information for Discriminative Clustering. - Yutong Wang, Clayton Scott:
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel. - Qiancheng Fu, Qingshan Xu, Yew Soon Ong, Wenbing Tao:
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction. - Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil:
Sublinear Algorithms for Hierarchical Clustering. - Soroush Ebadian, Gregory Kehne, Evi Micha, Ariel D. Procaccia, Nisarg Shah:
Is Sortition Both Representative and Fair? - Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim:
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. - Noémie Périvier, Vineet Goyal:
Dynamic pricing and assortment under a contextual MNL demand. - Marie Maros, Gesualdo Scutari:
DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery. - Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab:
Pseudo-Riemannian Graph Convolutional Networks. - Philippe Weinzaepfel, Vincent Leroy, Thomas Lucas, Romain Brégier, Yohann Cabon, Vaibhav Arora, Leonid Antsfeld, Boris Chidlovskii, Gabriela Csurka, Jérôme Revaud:
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion. - Elías Abad-Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Sound and Complete Verification of Polynomial Networks. - Hoang Tran, Ashok Cutkosky:
Better SGD using Second-order Momentum. - Misha Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. - Changfeng Ma, Yang Yang, Jie Guo, Fei Pan, Chongjun Wang, Yanwen Guo:
Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network. - Chen Chen, Yuchen Liu, Xingjun Ma, Lingjuan Lyu:
CalFAT: Calibrated Federated Adversarial Training with Label Skewness. - Markus Hiller, Rongkai Ma, Mehrtash Harandi, Tom Drummond:
Rethinking Generalization in Few-Shot Classification. - Peng Ye, Shengji Tang, Baopu Li, Tao Chen, Wanli Ouyang:
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing. - Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu:
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations. - Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. - Lior Danon, Dan Garber:
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. - Matthias Schultheis, Constantin A. Rothkopf, Heinz Koeppl:
Reinforcement Learning with Non-Exponential Discounting. - Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang:
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? - Amin Jaber, Adèle H. Ribeiro, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness. - Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N. M. Anoop Krishnan, Sayan Ranu:
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems. - Gerdus Benadè, Daniel Halpern, Alexandros Psomas:
Dynamic Fair Division with Partial Information. - Veit D. Wild, Robert Hu, Dino Sejdinovic:
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. - Samuel Acquaviva, Yewen Pu, Marta Kryven, Theodoros Sechopoulos, Catherine Wong, Gabrielle E. Ecanow, Maxwell I. Nye, Michael Henry Tessler, Josh Tenenbaum:
Communicating Natural Programs to Humans and Machines. - Daniel McDuff, Miah Wander, Xin Liu, Brian L. Hill, Javier Hernandez, Jonathan Lester, Tadas Baltrusaitis:
SCAMPS: Synthetics for Camera Measurement of Physiological Signals. - James Harrison, Luke Metz, Jascha Sohl-Dickstein:
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases. - Lingyu Gu, Yongqi Du, Yuan Zhang, Di Xie, Shiliang Pu, Robert C. Qiu, Zhenyu Liao:
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach. - Jason M. Altschuler, Kunal Talwar:
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss. - Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin S. Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam:
Theseus: A Library for Differentiable Nonlinear Optimization. - Dong-Hee Paek, Seung-Hyun Kong, Kevin Tirta Wijaya:
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions. - Xin-Chun Li, Wen-Shu Fan, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, De-Chuan Zhan:
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again. - Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra:
Solving Quantitative Reasoning Problems with Language Models. - Philip de Rijk, Lukas Schneider, Marius Cordts, Dariu Gavrila:
Structural Knowledge Distillation for Object Detection. - Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh, Mohsen Bayati:
Thompson Sampling Efficiently Learns to Control Diffusion Processes. - Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes:
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning. - Leyan Deng, Defu Lian, Chenwang Wu, Enhong Chen:
Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy. - Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim:
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. - Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Chen Change Loy:
Flare7K: A Phenomenological Nighttime Flare Removal Dataset. - Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen Wu, Yufeng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jindong Wang, Xing Xie, Yue Zhang:
USB: A Unified Semi-supervised Learning Benchmark for Classification. - Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob Foerster:
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. - Chengchang Liu, Luo Luo:
Quasi-Newton Methods for Saddle Point Problems. - Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik:
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency. - Kailas Vodrahalli, Tobias Gerstenberg, James Y. Zou:
Uncalibrated Models Can Improve Human-AI Collaboration. - Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil:
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. - Jung-Hee Kim, Junhwa Hur, Tien Phuoc Nguyen, Seong-Gyun Jeong:
Self-supervised surround-view depth estimation with volumetric feature fusion. - Bonifaz Stuhr, Johann Haselberger, Julian Gebele:
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains. - Markus Hiller, Mehrtash Harandi, Tom Drummond:
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation. - Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang:
Oracle-Efficient Online Learning for Smoothed Adversaries. - Haoran Xu, Li Jiang, Jianxiong Li, Xianyuan Zhan:
A Policy-Guided Imitation Approach for Offline Reinforcement Learning. - Ziang Song, Song Mei, Yu Bai:
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. - Selena Ling, Nicholas Sharp, Alec Jacobson:
VectorAdam for Rotation Equivariant Geometry Optimization. - Rahul Jain, Georgios Piliouras, Ryann Sim:
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence. - Daouda Sow, Kaiyi Ji, Yingbin Liang:
On the Convergence Theory for Hessian-Free Bilevel Algorithms. - Sékou-Oumar Kaba, Siamak Ravanbakhsh:
Equivariant Networks for Crystal Structures. - Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa:
A General Framework for Auditing Differentially Private Machine Learning. - Masaaki Nishino, Kengo Nakamura, Norihito Yasuda:
Generalization Analysis on Learning with a Concurrent Verifier. - Kai Sheng Tai, Tai-Peng Tian, Ser Nam Lim:
Spartan: Differentiable Sparsity via Regularized Transportation. - Jianwei Yang, Chunyuan Li, Xiyang Dai, Jianfeng Gao:
Focal Modulation Networks. - Qing Li, Yu-Shen Liu, Jin-San Cheng, Cheng Wang, Yi Fang, Zhizhong Han:
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces. - Daniel Bienstock, Minchan Jeong, Apurv Shukla, Se-Young Yun:
Robust Streaming PCA. - Chengan He, Jun Saito, James Zachary, Holly E. Rushmeier, Yi Zhou:
NeMF: Neural Motion Fields for Kinematic Animation. - Ehsan Variani, Ke Wu, Michael D. Riley, David Rybach, Matt Shannon, Cyril Allauzen:
Global Normalization for Streaming Speech Recognition in a Modular Framework. - Yiqun Mei, Pengfei Guo, Mo Zhou, Vishal Patel:
Resource-Adaptive Federated Learning with All-In-One Neural Composition. - Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi:
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression. - Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He:
Your Transformer May Not be as Powerful as You Expect. - Rongqin Chen, Shenghui Zhang, Leong Hou U, Ye Li:
Redundancy-Free Message Passing for Graph Neural Networks. - Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto:
Diffusion-LM Improves Controllable Text Generation. - Paul Novello, Thomas Fel, David Vigouroux:
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. - Beomsu Kim, Jong Chul Ye:
Energy-Based Contrastive Learning of Visual Representations. - Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang:
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. - Yuchen Xiao, Weihao Tan, Christopher Amato:
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning. - Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas A. Funkhouser, Bharath Hariharan, Serge J. Belongie:
Polynomial Neural Fields for Subband Decomposition and Manipulation. - Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, Colin White:
On the Generalizability and Predictability of Recommender Systems. - Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton:
Optimal Rates for Regularized Conditional Mean Embedding Learning. - Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing:
Divert More Attention to Vision-Language Tracking. - Shangquan Sun, Wenqi Ren, Tao Wang, Xiaochun Cao:
Rethinking Image Restoration for Object Detection. - Elias Frantar, Dan Alistarh:
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. - Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Challenging Common Assumptions in Convex Reinforcement Learning. - Jiayi Shen, Zehao Xiao, Xiantong Zhen, Cees Snoek, Marcel Worring:
Association Graph Learning for Multi-Task Classification with Category Shifts. - Jiazhi Guan, Hang Zhou, Zhibin Hong, Errui Ding, Jingdong Wang, Chengbin Quan, Youjian Zhao:
Delving into Sequential Patches for Deepfake Detection. - Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. - Franz Scherr, Qinghai Guo, Timoleon Moraitis:
Self-Supervised Learning Through Efference Copies. - Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue Lu, Jeffrey Pennington:
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression. - Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, Xinyi Le:
A Unified Model for Multi-class Anomaly Detection. - Jaekyeom Kim, Seohong Park, Gunhee Kim:
Constrained GPI for Zero-Shot Transfer in Reinforcement Learning. - Yanbo Xu, Alind Khare, Glenn Matlin, Monish Ramadoss, Rishikesan Kamaleswaran, Chao Zhang, Alexey Tumanov:
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. - Di Lin, Xin Wang, Jia Shen, Renjie Zhang, Ruonan Liu, Miaohui Wang, Wuyuan Xie, Qing Guo, Ping Li:
Generative Status Estimation and Information Decoupling for Image Rain Removal. - Ingvar M. Ziemann, Stephen Tu:
Learning with little mixing. - Rui Ding, Kehua Guo, Xiangyuan Zhu, Zheng Wu, Liwei Wang:
ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition. - Mukund Varma T., Xuxi Chen, Zhenyu Zhang, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang:
Sparse Winning Tickets are Data-Efficient Image Recognizers. - Eleonora Misino, Giuseppe Marra, Emanuele Sansone:
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming. - Lei Wu, Mingze Wang, Weijie Su:
The alignment property of SGD noise and how it helps select flat minima: A stability analysis. - Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei:
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks. - Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao, Ming C. Lin, Xiaodi Wu:
Differentiable Analog Quantum Computing for Optimization and Control. - Yafei Yang, Bo Yang:
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images. - Sebastian Dalleiger, Jilles Vreeken:
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. - Ye Du, Yujun Shen, Haochen Wang, Jingjing Fei, Wei Li, Liwei Wu, Rui Zhao, Zehua Fu, Qingjie Liu:
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation. - Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu:
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. - Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, Muhan Zhang:
How Powerful are K-hop Message Passing Graph Neural Networks. - Aravind Reddy, Zhao Song, Lichen Zhang:
Dynamic Tensor Product Regression. - Sohir Maskey, Ron Levie, Yunseok Lee, Gitta Kutyniok:
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs. - Alexandros Psomas, Ariel Schvartzman, S. Matthew Weinberg:
On Infinite Separations Between Simple and Optimal Mechanisms. - Huiwen Jia, Cong Shi, Siqian Shen:
Online Learning and Pricing for Network Revenue Management with Reusable Resources. - Jonathan Laurent, André Platzer:
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis. - Denizalp Goktas, Amy Greenwald:
Exploitability Minimization in Games and Beyond. - Yue Hu, Shaoheng Fang, Zixing Lei, Yiqi Zhong, Siheng Chen:
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps. - Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei, Reihaneh Zohrabi, Mohammad Taghi Manzuri, Mohammad Hossein Rohban:
Your Out-of-Distribution Detection Method is Not Robust! - Piyush Raikwar, Deepak Mishra:
Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation. - Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi:
NaturalProver: Grounded Mathematical Proof Generation with Language Models. - Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang:
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. - Gregory Canal, Blake Mason, Ramya Korlakai Vinayak, Robert Nowak:
One for All: Simultaneous Metric and Preference Learning over Multiple Users. - Ze Wang, Yipin Zhou, Rui Wang, Tsung-Yu Lin, Ashish Shah, Ser Nam Lim:
Few-Shot Fast-Adaptive Anomaly Detection. - Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, Yifan Liu:
SegViT: Semantic Segmentation with Plain Vision Transformers. - Julián Tachella, Dongdong Chen, Mike E. Davies:
Unsupervised Learning From Incomplete Measurements for Inverse Problems. - Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming intrinsic rewards via constrained optimization. - Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, Maosong Sun:
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks. - Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. - Tomoya Murata, Taiji Suzuki:
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. - Yao Qiang, Deng Pan, Chengyin Li, Xin Li, Rhongho Jang, Dongxiao Zhu:
AttCAT: Explaining Transformers via Attentive Class Activation Tokens. - Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, Bing Liu:
A Theoretical Study on Solving Continual Learning. - Yixing Xu, Xinghao Chen, Yunhe Wang:
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons. - Fanghui Liu, Luca Viano, Volkan Cevher:
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration. - Zhaoqiang Liu, Xinshao Wang, Jiulong Liu:
Misspecified Phase Retrieval with Generative Priors. - Tian Yu Liu, Baharan Mirzasoleiman:
Data-Efficient Augmentation for Training Neural Networks. - Ziyi Zhang, Weikai Chen, Hui Cheng, Zhen Li, Siyuan Li, Liang Lin, Guanbin Li:
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning. - Yuanpei Chen, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuan Jiang, Zongqing Lu, Stephen McAleer, Hao Dong, Song-Chun Zhu, Yaodong Yang:
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning. - Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Ankit Pensia:
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. - Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines:
Local-Global MCMC kernels: the best of both worlds. - Adam Haber, Elad Schneidman:
The computational and learning benefits of Daleian neural networks. - Kevin Frans, Lisa B. Soros, Olaf Witkowski:
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders. - Luis Herrmann, Maximilian Granz, Tim Landgraf:
Chaotic Dynamics are Intrinsic to Neural Network Training with SGD. - Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. - Gauthier Guinet, Saurabh Amin, Patrick Jaillet:
Effective Dimension in Bandit Problems under Censorship. - Tessa Han, Suraj Srinivas, Himabindu Lakkaraju:
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. - Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati:
Accelerating Certified Robustness Training via Knowledge Transfer. - Zelun Luo, Zane Durante, Linden Li, Wanze Xie, Ruochen Liu, Emily Jin, Zhuoyi Huang, Lun Yu Li, Jiajun Wu, Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li:
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing. - Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy M. Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William R. Gray Roncal, Erik C. Johnson, Eva L. Dyer:
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. - Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. - Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. - Zikui Cai, Chengyu Song, Srikanth Krishnamurthy, Amit Roy-Chowdhury, Salman Asif:
Blackbox Attacks via Surrogate Ensemble Search. - Libin Zhu, Chaoyue Liu, Misha Belkin:
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture. - Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang:
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking. - Philipp Holl, Vladlen Koltun, Nils Thuerey:
Scale-invariant Learning by Physics Inversion. - Róbert Busa-Fekete, Heejin Choi, Krzysztof Dembczynski, Claudio Gentile, Henry Reeve, Balázs Szörényi:
Regret Bounds for Multilabel Classification in Sparse Label Regimes. - Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao:
Sample Constrained Treatment Effect Estimation. - Xuanli He, Qiongkai Xu, Yi Zeng, Lingjuan Lyu, Fangzhao Wu, Jiwei Li, Ruoxi Jia:
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks. - Gabriele Cesa, Arash Behboodi, Taco S. Cohen, Max Welling:
On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane. - Nishanth Dikkala, Sankeerth Rao Karingula, Raghu Meka, Jelani Nelson, Rina Panigrahy, Xin Wang:
Sketching based Representations for Robust Image Classification with Provable Guarantees. - Siqi Shen, Mengwei Qiu, Jun Liu, Weiquan Liu, Yongquan Fu, Xinwang Liu, Cheng Wang:
ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization. - Eli N. Weinstein, Alan Nawzad Amin, Jonathan Frazer, Debora S. Marks:
Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness. - Hao Jiang, Yadong Mu:
Conditional Diffusion Process for Inverse Halftoning. - Rihab Gorsane, Omayma Mahjoub, Ruan de Kock, Roland Dubb, Siddarth Singh, Arnu Pretorius:
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL. - Dan Mikulincer, Daniel Reichman:
Size and depth of monotone neural networks: interpolation and approximation. - Jack Lindsey, Ashok Litwin-Kumar:
Action-modulated midbrain dopamine activity arises from distributed control policies. - Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. - Seiji Maekawa, Koki Noda, Yuya Sasaki, Makoto Onizuka:
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs. - Tian Tian, Kenny Young, Richard S. Sutton:
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions. - Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls:
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. - Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas:
NOMAD: Nonlinear Manifold Decoders for Operator Learning. - Yongri Piao, Chenyang Lu, Miao Zhang, Huchuan Lu:
Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels. - Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Ya-Lin Zhang, Feng Zhu, Longfei Li, Jun Zhou, Linbo Jiang:
Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding. - Kyeongwon Lee, Jaeyong Lee:
Asymptotic Properties for Bayesian Neural Network in Besov Space. - Arash Behboodi, Gabriele Cesa, Taco S. Cohen:
A PAC-Bayesian Generalization Bound for Equivariant Networks. - Shijun Zhang, Zuowei Shen, Haizhao Yang:
Neural Network Architecture Beyond Width and Depth. - Yabin Wang, Zhiwu Huang, Xiaopeng Hong:
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning. - Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Luowei Zhou, Yucheng Zhao, Yujia Xie, Ce Liu, Yu-Gang Jiang, Lu Yuan:
OmniVL: One Foundation Model for Image-Language and Video-Language Tasks. - Jianzhun Shao, Zhiqiang Lou, Hongchang Zhang, Yuhang Jiang, Shuncheng He, Xiangyang Ji:
Self-Organized Group for Cooperative Multi-agent Reinforcement Learning. - Anders Aamand, Justin Y. Chen, Piotr Indyk:
(Optimal) Online Bipartite Matching with Degree Information. - Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta:
Fairness in Federated Learning via Core-Stability. - Bing Su, Ji-Rong Wen:
Log-Polar Space Convolution Layers. - Haoran Li, Yang Weng, Hanghang Tong:
CoNSoLe: Convex Neural Symbolic Learning. - Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. - Qihua Zhou, Song Guo, Yi Liu, Jie Zhang, Jiewei Zhang, Tao Guo, Zhenda Xu, Xun Liu, Zhihao Qu:
Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning. - Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, Joost van de Weijer:
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation. - Minhao Liu, Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, Qiang Xu:
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction. - Rati Devidze, Parameswaran Kamalaruban, Adish Singla:
Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards. - David Lindner, Andreas Krause, Giorgia Ramponi:
Active Exploration for Inverse Reinforcement Learning. - John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. - Alexander Ororbia, Ankur Arjun Mali, C. Lee Giles, Daniel Kifer:
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting. - Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu:
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences. - Lecheng Kong, Yixin Chen, Muhan Zhang:
Geodesic Graph Neural Network for Efficient Graph Representation Learning. - Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta:
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. - Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith:
On Privacy and Personalization in Cross-Silo Federated Learning. - Jianhong Wang, Yuan Zhang, Yunjie Gu, Tae-Kyun Kim:
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning. - Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio:
Trajectory balance: Improved credit assignment in GFlowNets. - Andrew Wagenmaker, Kevin G. Jamieson:
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. - Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Kiana Ehsani, Jordi Salvador, Winson Han, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi:
🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation. - Xiang Cheng, Jingzhao Zhang, Suvrit Sra:
Efficient Sampling on Riemannian Manifolds via Langevin MCMC. - Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, Youzuo Lin:
OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion. - Chenqing Hua, Guillaume Rabusseau, Jian Tang:
High-Order Pooling for Graph Neural Networks with Tensor Decomposition. - Babak Rahmani, Demetri Psaltis, Christophe Moser:
Natural image synthesis for the retina with variational information bottleneck representation. - Benjamin Eysenbach, Soumith Udatha, Russ Salakhutdinov, Sergey Levine:
Imitating Past Successes can be Very Suboptimal. - Zhenyu Sun, Ermin Wei:
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning. - Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Zhou Qin, Wenwu Zhu:
Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift. - Jianchuan Ding, Bo Dong, Felix Heide, Yufei Ding, Yunduo Zhou, Baocai Yin, Xin Yang:
Biologically Inspired Dynamic Thresholds for Spiking Neural Networks. - Yehui Tang, Junchi Yan:
GraphQNTK: Quantum Neural Tangent Kernel for Graph Data. - Penghao Wu, Xiaosong Jia, Li Chen, Junchi Yan, Hongyang Li, Yu Qiao:
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline. - Bolivar Solarte, Chin-Hsuan Wu, Yueh-Cheng Liu, Yi-Hsuan Tsai, Min Sun:
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning. - Bingqing Song, Ioannis C. Tsaknakis, Chung-Yiu Yau, Hoi-To Wai, Mingyi Hong:
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity. - Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag:
Falsification before Extrapolation in Causal Effect Estimation. - Karush Suri, Xiao Qi Shi, Konstantinos N. Plataniotis, Yuri A. Lawryshyn:
Surprise Minimizing Multi-Agent Learning with Energy-based Models. - Zhengyu Li, Xuan Tang, Zihao Xu, Xihao Wang, Hui Yu, Mingsong Chen, Xian Wei:
Geodesic Self-Attention for 3D Point Clouds. - Sachin Goyal, Mingjie Sun, Aditi Raghunathan, J. Zico Kolter:
Test Time Adaptation via Conjugate Pseudo-labels. - Reinmar J. Kobler, Jun-ichiro Hirayama, Qibin Zhao, Motoaki Kawanabe:
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG. - Sudipta Paul, Amit Roy-Chowdhury, Anoop Cherian:
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments. - Swami Sankaranarayanan, Anastasios Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola:
Semantic uncertainty intervals for disentangled latent spaces. - Jelena Diakonikolas, Chenghui Li, Swati Padmanabhan, Chaobing Song:
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data. - Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P. Wipf, Yanwei Fu, Zheng Zhang:
Self-supervised Amodal Video Object Segmentation. - Audrey Huang, Nan Jiang:
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions. - Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Disentangling Transfer in Continual Reinforcement Learning. - Jihoon Tack, Jongjin Park, Hankook Lee, Jaeho Lee, Jinwoo Shin:
Meta-Learning with Self-Improving Momentum Target. - Steven Yin, Shipra Agrawal, Assaf Zeevi:
Online Allocation and Learning in the Presence of Strategic Agents. - Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Learning Contrastive Embedding in Low-Dimensional Space. - Zhihan Xiong, Ruoqi Shen, Qiwen Cui, Maryam Fazel, Simon S. Du:
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes. - Xiyuan Li, Zou Xin, Weiwei Liu:
Defending Against Adversarial Attacks via Neural Dynamic System. - Yang Song, Qiyu Kang, Sijie Wang, Kai Zhao, Wee Peng Tay:
On the Robustness of Graph Neural Diffusion to Topology Perturbations. - Qian Huang, Hongyu Ren, Jure Leskovec:
Few-shot Relational Reasoning via Connection Subgraph Pretraining. - Darius Muglich, Christian Schröder de Witt, Elise van der Pol, Shimon Whiteson, Jakob N. Foerster:
Equivariant Networks for Zero-Shot Coordination. - Rong Yin, Yong Liu, Weiping Wang, Dan Meng:
Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means. - Whiyoung Jung, Myungsik Cho, Jongeui Park, Youngchul Sung:
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability. - Manel Baradad, Chun-Fu Richard Chen, Jonas Wulff, Tongzhou Wang, Rogério Feris, Antonio Torralba, Phillip Isola:
Procedural Image Programs for Representation Learning. - Kyungmin Lee, Jinwoo Shin:
RényiCL: Contrastive Representation Learning with Skew Rényi Divergence. - Moses Charikar, Zhihao Jiang, Kirankumar Shiragur, Aaron Sidford:
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood. - Cheng-Fu Yang, Yao-Hung Hubert Tsai, Wan-Cyuan Fan, Russ Salakhutdinov, Louis-Philippe Morency, Frank Wang:
Paraphrasing Is All You Need for Novel Object Captioning. - Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora:
Adversarial Robustness is at Odds with Lazy Training. - Myong Chol Jung, He Zhao, Joanna Dipnall, Belinda Gabbe, Lan Du:
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture. - Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele:
Motion Transformer with Global Intention Localization and Local Movement Refinement. - Yu-Guan Hsieh, Kimon Antonakopoulos, Volkan Cevher, Panayotis Mertikopoulos:
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation. - Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. - Jian-Wei Zhang, Yifan Sun, Yi Yang, Wei Chen:
Feature-Proxy Transformer for Few-Shot Segmentation. - Matteo Sesia, Stefano Favaro:
Conformal Frequency Estimation with Sketched Data. - Pablo Moreno-Muñoz, Cilie W. Feldager, Søren Hauberg:
Revisiting Active Sets for Gaussian Process Decoders. - Lukas Braun, Clémentine Dominé, James Fitzgerald, Andrew M. Saxe:
Exact learning dynamics of deep linear networks with prior knowledge. - Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang:
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness. - Fadi Hamad, Oliver Hinder:
A consistently adaptive trust-region method. - Weihan Li, Yu Qi, Gang Pan:
Online Neural Sequence Detection with Hierarchical Dirichlet Point Process. - Changbao Wang, Dandan Zheng, Yuanliu Liu, Liang Li:
Leveraging Inter-Layer Dependency for Post -Training Quantization. - Paul Bertens, Seong-Whan Lee:
Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons. - Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye:
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. - Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover:
CyCLIP: Cyclic Contrastive Language-Image Pretraining. - Vijay Vasudevan, Benjamin Caine, Raphael Gontijo Lopes, Sara Fridovich-Keil, Rebecca Roelofs:
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet. - Jianhui Liu, Yukang Chen, Xiaoqing Ye, Zhuotao Tian, Xiao Tan, Xiaojuan Qi:
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection. - Aniket Das, Bernhard Schölkopf, Michael Muehlebach:
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization. - Arthur Jacot, Eugene Golikov, Clément Hongler, Franck Gabriel:
Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity. - Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Set-based Meta-Interpolation for Few-Task Meta-Learning. - Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun:
Non-deep Networks. - Meyer Scetbon, Marco Cuturi:
Low-rank Optimal Transport: Approximation, Statistics and Debiasing. - Zhengyi Luo, Shun Iwase, Ye Yuan, Kris Kitani:
Embodied Scene-aware Human Pose Estimation. - Kristian Georgiev, Samuel B. Hopkins:
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation. - ChangYong Oh, Roberto Bondesan, Efstratios Gavves, Max Welling:
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel. - Charlotte Bunne, Andreas Krause, Marco Cuturi:
Supervised Training of Conditional Monge Maps. - Harald Strömfelt, Luke Dickens, Artur S. d'Avila Garcez, Alessandra Russo:
Formalizing Consistency and Coherence of Representation Learning. - Satoshi Hayakawa, Harald Oberhauser, Terry J. Lyons:
Positively Weighted Kernel Quadrature via Subsampling. - Lukas Prantl, Benjamin Ummenhofer, Vladlen Koltun, Nils Thuerey:
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics. - Lichao Zhang, Ruiqi Li, Shoutong Wang, Liqun Deng, Jinglin Liu, Yi Ren, Jinzheng He, Rongjie Huang, Jieming Zhu, Xiao Chen, Zhou Zhao:
M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus. - Luning Sun, Daniel Huang, Hao Sun, Jian-Xun Wang:
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. - Julien Klaus, Niklas Merk, Konstantin Wiedom, Sören Laue, Joachim Giesen:
Convexity Certificates from Hessians. - Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li, Chao Yue, Yuankai Zhang:
FR: Folded Rationalization with a Unified Encoder. - Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification. - Fan Yang, Lin Guo, Zhi Chen, Wenbing Tao:
One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration. - Dandan Guo, Long Tian, He Zhao, Mingyuan Zhou, Hongyuan Zha:
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport. - Sidi Lu, Tao Meng, Nanyun Peng:
InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model. - Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trinh, Erik Vee:
Weighted Distillation with Unlabeled Examples. - Yimeng Chen, Ruibin Xiong, Zhi-Ming Ma, Yanyan Lan:
When Does Group Invariant Learning Survive Spurious Correlations? - Chengliang Zhong, Peixing You, Xiaoxue Chen, Hao Zhao, Fuchun Sun, Guyue Zhou, Xiaodong Mu, Chuang Gan, Wenbing Huang:
SNAKE: Shape-aware Neural 3D Keypoint Field. - Hidenori Iwakiri, Yuhang Wang, Shinji Ito, Akiko Takeda:
Single Loop Gaussian Homotopy Method for Non-convex Optimization. - Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. - Max Daniels, Cédric Gerbelot, Florent Krzakala, Lenka Zdeborová:
Multi-layer State Evolution Under Random Convolutional Design. - Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Y. Zhao, Andrew M. Dai, Zhifeng Chen, Quoc V. Le, James Laudon:
Mixture-of-Experts with Expert Choice Routing. - Enric Boix-Adserà, Hannah Lawrence, George Stepaniants, Philippe Rigollet:
GULP: a prediction-based metric between representations. - Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos, Emmanouil V. Vlatakis-Gkaragkounis:
On the convergence of policy gradient methods to Nash equilibria in general stochastic games. - Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu:
SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning. - Shariq Iqbal, Robby Costales, Fei Sha:
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks. - Kuan-Lin Chen, Harinath Garudadri, Bhaskar D. Rao:
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions. - Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. - Wenbin Song, Mingrui Zhang, Joseph G. Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew D. Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen, Jun Wang:
M2N: Mesh Movement Networks for PDE Solvers. - Kevin D. Smith, Francesco Seccamonte, Ananthram Swami, Francesco Bullo:
Physics-Informed Implicit Representations of Equilibrium Network Flows. - Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott P. MacLachlan, Luke N. Olson, Matthew West:
Learning Interface Conditions in Domain Decomposition Solvers. - Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, Shuangjia Zheng:
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction. - Asif Khan, Amos J. Storkey:
Hamiltonian Latent Operators for content and motion disentanglement in image sequences. - Mingguo He, Zhewei Wei, Ji-Rong Wen:
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited. - Wenkai Xu, Gesine D. Reinert:
A Kernelised Stein Statistic for Assessing Implicit Generative Models. - Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, Siliang Tang:
Fine-Grained Semantically Aligned Vision-Language Pre-Training. - Yichuan Deng, Zhao Song, Omri Weinstein, Ruizhe Zhang:
Fast Distance Oracles for Any Symmetric Norm. - Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. - Surbhi Goel, Sham M. Kakade, Adam Kalai, Cyril Zhang:
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. - Yejia Liu, Wang Zhu, Shaolei Ren:
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning. - Wei Mao, Miaomiao Liu, Richard I. Hartley, Mathieu Salzmann:
Contact-aware Human Motion Forecasting. - Sara Fridovich-Keil, Raphael Gontijo Lopes, Rebecca Roelofs:
Spectral Bias in Practice: The Role of Function Frequency in Generalization. - Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An:
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE. - Qiao Feng, Yebin Liu, Yu-Kun Lai, Jingyu Yang, Kun Li:
FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction. - Asaf B. Cassel, Alon Peled-Cohen, Tomer Koren:
Rate-Optimal Online Convex Optimization in Adaptive Linear Control. - Jian Wang, Chenhui Gou, Qiman Wu, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang:
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer. - Mayleen Cortez, Matthew Eichhorn, Christina Lee Yu:
Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge. - Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu:
Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis. - Zhiyu Zhu, Junhui Hou, Xianqiang Lyu:
Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds. - Adam Block, Max Simchowitz:
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. - Hongwei Chen, Douglas Hendry, Phillip Weinberg, Adrian E. Feiguin:
Systematic improvement of neural network quantum states using Lanczos. - Shusheng Xu, Huaijie Wang, Yi Wu:
Grounded Reinforcement Learning: Learning to Win the Game under Human Commands. - Xiaofeng Mao, Yuefeng Chen, Ranjie Duan, Yao Zhu, Gege Qi, Shaokai Ye, Xiaodan Li, Rong Zhang, Hui Xue:
Enhance the Visual Representation via Discrete Adversarial Training. - Thomas Orton, Damon Falck:
Trading Off Resource Budgets For Improved Regret Bounds. - Shengming Yuan, Qilong Zhang, Lianli Gao, Yaya Cheng, Jingkuan Song:
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks. - Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin F. Rousseau, Ying Ding, Zhangyang Wang:
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again. - Kevin Qinghong Lin, Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, Rong-Cheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou:
Egocentric Video-Language Pretraining. - Yan Chen, Perry Dong, Qinxun Bai, Maria Dimakopoulou, Wei Xu, Zhengyuan Zhou:
Society of Agents: Regret Bounds of Concurrent Thompson Sampling. - Eloïse Berthier, Ziad Kobeissi, Francis R. Bach:
A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning. - Kai Yan, Alexander G. Schwing, Yu-Xiong Wang:
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations. - Simone Bombari, Mohammad Hossein Amani, Marco Mondelli:
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization. - Mislav Balunovic, Dimitar I. Dimitrov, Nikola Jovanovic, Martin T. Vechev:
LAMP: Extracting Text from Gradients with Language Model Priors. - Nicholas A. Roy, Junkyung Kim, Neil C. Rabinowitz:
Explainability Via Causal Self-Talk. - Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, Shu Kong:
Continual Learning with Evolving Class Ontologies. - Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov:
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback. - Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora:
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. - Ho Huu Nghia Nguyen, Tan Nguyen, Huyen Vo, Stanley J. Osher, Thieu Vo:
Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method. - Erik Wijmans, Irfan Essa, Dhruv Batra:
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement. - Martin Weiss, Nasim Rahaman, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas:
Neural Attentive Circuits. - Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman, Jiajun Wu:
CLEVRER-Humans: Describing Physical and Causal Events the Human Way. - Yining Hong, Yilun Du, Chunru Lin, Josh Tenenbaum, Chuang Gan:
3D Concept Grounding on Neural Fields. - Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland:
Evaluating Graph Generative Models with Contrastively Learned Features. - Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger B. Grosse:
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. - Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel:
Implications of Model Indeterminacy for Explanations of Automated Decisions. - Ruofan Liu, Yun Lin, Xianglin Yang, Jin Song Dong:
Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective. - Mathieu Chalvidal, Thomas Serre, Rufin VanRullen:
Meta-Reinforcement Learning with Self-Modifying Networks. - Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Hao Liao, Zhongliang Yang, Yongfeng Huang, Xing Xie:
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. - An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua:
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering. - Zhixuan Yu, Linguang Zhang, Yuanlu Xu, Chengcheng Tang, Luan Tran, Cem Keskin, Hyun Soo Park:
Multiview Human Body Reconstruction from Uncalibrated Cameras. - Yan Huang, Yuming Wang, Yunan Zeng, Liang Wang:
MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching. - Rui Zhao, Ruiqin Xiong, Jing Zhao, Zhaofei Yu, Xiaopeng Fan, Tiejun Huang:
Learning Optical Flow from Continuous Spike Streams. - Zifan Shi, Yinghao Xu, Yujun Shen, Deli Zhao, Qifeng Chen, Dit-Yan Yeung:
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator. - Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko:
A Consistent and Differentiable Lp Canonical Calibration Error Estimator. - Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Ré, Stefano Ermon:
Transform Once: Efficient Operator Learning in Frequency Domain. - Jiacheng Wang, Dan Nicolae:
Fused Orthogonal Alternating Least Squares for Tensor Clustering. - Yatin Nandwani, Rishabh Ranjan, Mausam, Parag Singla:
A Solver-free Framework for Scalable Learning in Neural ILP Architectures. - Yang Jiao, Kai Yang, Dongjin Song:
Distributed Distributionally Robust Optimization with Non-Convex Objectives. - Jiujia Zhang, Ashok Cutkosky:
Parameter-free Regret in High Probability with Heavy Tails. - Michael Arbel, Julien Mairal:
Non-Convex Bilevel Games with Critical Point Selection Maps. - Salim I. Amoukou, Nicolas J.-B. Brunel:
Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor. - Andrés F. López-Lopera, François Bachoc, Olivier Roustant:
High-dimensional Additive Gaussian Processes under Monotonicity Constraints. - Larry Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary W. Ulissi, Brandon M. Wood:
Spherical Channels for Modeling Atomic Interactions. - Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Handcrafted Backdoors in Deep Neural Networks. - Fengyu Yang, Chenyang Ma, Jiacheng Zhang, Jing Zhu, Wenzhen Yuan, Andrew Owens:
Touch and Go: Learning from Human-Collected Vision and Touch. - Haobo Wang, Mingxuan Xia, Yixuan Li, Yuren Mao, Lei Feng, Gang Chen, Junbo Zhao:
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning. - Michael K. Cohen, Samuel Daulton, Michael A. Osborne:
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. - Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, Danqi Chen:
Recovering Private Text in Federated Learning of Language Models. - Kun Su, Mingfei Chen, Eli Shlizerman:
INRAS: Implicit Neural Representation for Audio Scenes. - Chenze Shao, Yang Feng:
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation. - Mattie Tesfaldet, Derek Nowrouzezahrai, Chris Pal:
Attention-based Neural Cellular Automata. - Ryosuke Kojima, Yuji Okamoto:
Learning Deep Input-Output Stable Dynamics. - Zeeshan Khan, C. V. Jawahar, Makarand Tapaswi:
Grounded Video Situation Recognition. - Eric Yang Yu, Zhizhen Qin, Min Kyung Lee, Sicun Gao:
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems. - Kartik Chandra, Audrey Xie, Jonathan Ragan-Kelley, Erik Meijer:
Gradient Descent: The Ultimate Optimizer. - Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. - Nicolai Engelmann, Heinz Koeppl:
Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains. - Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim:
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation. - Devansh Arpit, Huan Wang, Yingbo Zhou, Caiming Xiong:
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization. - Chuwei Wang, Shanda Li, Di He, Liwei Wang:
Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? - Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, Enhua Wu:
Vision GNN: An Image is Worth Graph of Nodes. - Zixian Ma, Rose E. Wang, Fei-Fei Li, Michael S. Bernstein, Ranjay Krishna:
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward. - Tong Mu, Yash Chandak, Tatsunori B. Hashimoto, Emma Brunskill:
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits. - Yutong Lin, Ze Liu, Zheng Zhang, Han Hu, Nanning Zheng, Stephen Lin, Yue Cao:
Could Giant Pre-trained Image Models Extract Universal Representations? - Kaixun Hua, Jiayang Ren, Yankai Cao:
A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree. - Albert Qiaochu Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygózdz, Piotr Milos, Yuhuai Wu, Mateja Jamnik:
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers. - Haotong Yang, Zhouchen Lin, Muhan Zhang:
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption. - Ta-Chung Chi, Ting-Han Fan, Peter J. Ramadge, Alexander Rudnicky:
KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation. - Matt Jordan, Jonathan Hayase, Alex Dimakis, Sewoong Oh:
Zonotope Domains for Lagrangian Neural Network Verification. - Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan:
Neural Basis Models for Interpretability. - Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang:
RecursiveMix: Mixed Learning with History. - Yuxin Wang, Zheng Xing, Wei W. Xing:
GAR: Generalized Autoregression for Multi-Fidelity Fusion. - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. - Ehsan Saleh, Saba Ghaffari, Timothy Bretl, Matthew West:
Truly Deterministic Policy Optimization. - Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Derek Hoiem, Shih-Fu Chang, Mohit Bansal, Heng Ji:
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners. - Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Yuanqing Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry:
3DB: A Framework for Debugging Computer Vision Models. - Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. - Songkai Xue, Yuekai Sun, Mikhail Yurochkin:
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. - Laurent Meunier, Raphael Ettedgui, Rafael Pinot, Yann Chevaleyre, Jamal Atif:
Towards Consistency in Adversarial Classification. - Shayan Shekarforoush, David B. Lindell, David J. Fleet, Marcus A. Brubaker:
Residual Multiplicative Filter Networks for Multiscale Reconstruction. - Jinli Liao, Yikang Ding, Yoli Shavit, Dihe Huang, Shihao Ren, Jia Guo, Wensen Feng, Kai Zhang:
WT-MVSNet: Window-based Transformers for Multi-view Stereo. - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. - Yuwei Fu, Di Wu, Benoit Boulet:
A Closer Look at Offline RL Agents. - Xin Wang, Shengfei Lyu, Xingyu Wu, Tianhao Wu, Huanhuan Chen:
Generalization Bounds for Estimating Causal Effects of Continuous Treatments. - Sebastian G. Gruber, Florian Buettner:
Better Uncertainty Calibration via Proper Scores for Classification and Beyond. - Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet:
Video Diffusion Models. - Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Formulating Robustness Against Unforeseen Attacks. - Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Timo Bremer:
Single Model Uncertainty Estimation via Stochastic Data Centering. - Navid Ansari, Hans-Peter Seidel, Nima Vahidi Ferdowsi, Vahid Babaei:
Autoinverse: Uncertainty Aware Inversion of Neural Networks. - Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines:
FedPop: A Bayesian Approach for Personalised Federated Learning. - Haoru Tan, Sitong Wu, Jimin Pi:
Semantic Diffusion Network for Semantic Segmentation. - Aoran Wang, Jun Pang:
Iterative Structural Inference of Directed Graphs. - Aryan Pedawi, Pawel Gniewek, Chaoyi Chang, Brandon M. Anderson, Henry van den Bedem:
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries. - Vladimir Fomenko, Ismail Elezi, Deva Ramanan, Laura Leal-Taixé, Aljosa Osep:
Learning to Discover and Detect Objects. - Jinho Choo, Yeong-Dae Kwon, Jihoon Kim, Jeongwoo Jae, André Hottung, Kevin Tierney, Youngjune Gwon:
Simulation-guided Beam Search for Neural Combinatorial Optimization. - Pawel Lorek, Rafal Nowak, Tomasz Trzcinski, Maciej Zieba:
FlowHMM: Flow-based continuous hidden Markov models. - Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann:
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs. - Adrien Bardes, Jean Ponce, Yann LeCun:
VICRegL: Self-Supervised Learning of Local Visual Features. - Anna Kuzina, Max Welling, Jakub M. Tomczak:
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC. - Edoardo Cetin, Oya Çeliktutan:
Policy Gradient With Serial Markov Chain Reasoning. - Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang:
DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection. - Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng, Bolei Zhou:
Human-AI Shared Control via Policy Dissection. - Yang Li, Yichuan Mo, Liangliang Shi, Junchi Yan:
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space. - Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, Srinath Sridhar:
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model. - Changan Chen, Carl Schissler, Sanchit Garg, Philip Kobernik, Alexander Clegg, Paul Calamia, Dhruv Batra, Philip W. Robinson, Kristen Grauman:
SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning. - Nicholas Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila, Spencer Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic Sala:
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels. - Apostolos Avranas, Marios Kountouris:
Towards Disentangling Information Paths with Coded ResNeXt. - Yuzhou Chen, Yulia R. Gel, H. Vincent Poor:
Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting. - Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski:
Are Defenses for Graph Neural Networks Robust? - Yucheng Lu, Wentao Guo, Christopher De Sa:
GraB: Finding Provably Better Data Permutations than Random Reshuffling. - Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse:
Amortized Proximal Optimization. - Chuhan Xie, Zhihua Zhang:
A Statistical Online Inference Approach in Averaged Stochastic Approximation. - Chuanyang Zheng, Zheyang Li, Kai Zhang, Zhi Yang, Wenming Tan, Jun Xiao, Ye Ren, Shiliang Pu:
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization. - Dilip Arumugam, Benjamin Van Roy:
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. - Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès:
Giga-scale Kernel Matrix-Vector Multiplication on GPU. - Yongsen Mao, Yiming Zhang, Hanxiao Jiang, Angel X. Chang, Manolis Savva:
MultiScan: Scalable RGBD scanning for 3D environments with articulated objects. - David Alvarez-Melis, Vikas Garg, Adam Kalai:
Are GANs overkill for NLP? - Yipei Wang, Xiaoqian Wang:
"Why Not Other Classes?": Towards Class-Contrastive Back-Propagation Explanations. - Jingdong Zhang, Qunxi Zhu, Wei Lin:
Neural Stochastic Control. - Long Yang, Jiaming Ji, Juntao Dai, Linrui Zhang, Binbin Zhou, Pengfei Li, Yaodong Yang, Gang Pan:
Constrained Update Projection Approach to Safe Policy Optimization. - Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, Hang Xu:
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection. - Gal Vardi, Ohad Shamir, Nati Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. - Tao Liu, P. R. Kumar, Ruida Zhou, Xi Liu:
Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales. - Zhiwei Hao, Jianyuan Guo, Ding Jia, Kai Han, Yehui Tang, Chao Zhang, Han Hu, Yunhe Wang:
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation. - Jiaqi Han, Wenbing Huang, Tingyang Xu, Yu Rong:
Equivariant Graph Hierarchy-Based Neural Networks. - Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch:
Learning interacting dynamical systems with latent Gaussian process ODEs. - Mohit Prabhushankar, Kiran Kokilepersaud, Yash-Yee Logan, Stephanie Trejo Corona, Ghassan AlRegib, Charles C. Wykoff:
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics. - Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno C. da Silva, Emma Brunskill, Philip S. Thomas:
Off-Policy Evaluation for Action-Dependent Non-stationary Environments. - Zhiyuan Li, Tianhao Wang, Dingli Yu:
Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay. - Liang Yang, Lina Kang, Qiuliang Zhang, Mengzhe Li, Bingxin Niu, Dongxiao He, Zhen Wang, Chuan Wang, Xiaochun Cao, Yuanfang Guo:
OPEN: Orthogonal Propagation with Ego-Network Modeling. - Yifei Wang, Tavor Z. Baharav, Yanjun Han, Jiantao Jiao, David Tse:
Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits. - Daniel M. Ziegler, Seraphina Nix, Lawrence Chan, Tim Bauman, Peter Schmidt-Nielsen, Tao Lin, Adam Scherlis, Noa Nabeshima, Ben Weinstein-Raun, Daniel de Haas, Buck Shlegeris, Nate Thomas:
Adversarial training for high-stakes reliability. - Chunyuan Li, Haotian Liu, Liunian Harold Li, Pengchuan Zhang, Jyoti Aneja, Jianwei Yang, Ping Jin, Houdong Hu, Zicheng Liu, Yong Jae Lee, Jianfeng Gao:
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models. - Akshay Mete, Rahul Singh, P. R. Kumar:
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems. - Jie Xiao, Xueyang Fu, Feng Wu, Zheng-Jun Zha:
Stochastic Window Transformer for Image Restoration. - Yue Xing, Qifan Song, Guang Cheng:
Phase Transition from Clean Training to Adversarial Training. - Daoyuan Chen, Dawei Gao, Weirui Kuang, Yaliang Li, Bolin Ding:
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning. - Sehoon Kim, Amir Gholami, Albert E. Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer:
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. - Guan-Horng Liu, Tianrong Chen, Oswin So, Evangelos A. Theodorou:
Deep Generalized Schrödinger Bridge. - Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu:
Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models. - Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart:
Learning sparse features can lead to overfitting in neural networks. - Karish Grover, S. M. Phaneendra Angara, Md. Shad Akhtar, Tanmoy Chakraborty:
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification. - Thomas Fel, Ivan F. Rodriguez Rodriguez, Drew Linsley, Thomas Serre:
Harmonizing the object recognition strategies of deep neural networks with humans. - Yiqun Hu, David Simchi-Levi, Zhenzhen Yan:
Learning Mixed Multinomial Logits with Provable Guarantees. - Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger:
Defining and Characterizing Reward Gaming. - Qi Chen, Chaorui Deng, Qi Wu:
Learning Distinct and Representative Modes for Image Captioning. - Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz:
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. - Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam:
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations. - Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetic, Mario Lucic, Leonidas J. Guibas, Klaus Greff, Thomas Kipf:
Object Scene Representation Transformer. - Abhilash Reddy Chenreddy, Nymisha Bandi, Erick Delage:
Data-Driven Conditional Robust Optimization. - Lianhui Qin, Sean Welleck, Daniel Khashabi, Yejin Choi:
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics. - Yirui Zhang, Siwei Wang, Zhixuan Fang:
Matching in Multi-arm Bandit with Collision. - Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, Neil Houlsby:
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts. - Atsutoshi Kumagai, Tomoharu Iwata, Yasutoshi Ida, Yasuhiro Fujiwara:
Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion. - Yuri R. Fonseca, Yuri F. Saporito:
Statistical Learning and Inverse Problems: A Stochastic Gradient Approach. - Xueying Ding, Lingxiao Zhao, Leman Akoglu:
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution. - Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal:
TVLT: Textless Vision-Language Transformer. - Cheng Chi, Amine Mohamed Aboussalah, Elias B. Khalil, Juyoung Wang, Zoha Sherkat-Masoumi:
A Deep Reinforcement Learning Framework for Column Generation. - Djordje Miladinovic, Kumar Shridhar, Kushal Jain, Max B. Paulus, Joachim M. Buhmann, Carl Allen:
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs. - Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli:
On the Adversarial Robustness of Mixture of Experts. - Rayan Mazouz, Karan Muvvala, Akash Ratheesh, Luca Laurenti, Morteza Lahijanian:
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions. - Vasilis Livanos:
Simple and Optimal Greedy Online Contention Resolution Schemes. - Xin Lyu:
Composition Theorems for Interactive Differential Privacy. - Felix Biggs, Valentina Zantedeschi, Benjamin Guedj:
On Margins and Generalisation for Voting Classifiers. - Weixin Chen, Baoyuan Wu, Haoqian Wang:
Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples. - Zhenting Wang, Kai Mei, Hailun Ding, Juan Zhai, Shiqing Ma:
Rethinking the Reverse-engineering of Trojan Triggers. - Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, Jianzhu Ma:
Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures. - Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song:
Learning single-index models with shallow neural networks. - Vudtiwat Ngampruetikorn, David J. Schwab:
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. - Xuanhong Chen, Kairui Feng, Naiyuan Liu, Bingbing Ni, Yifan Lu, Zhengyan Tong, Ziang Liu:
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling. - Yongchao Zhou, Ehsan Nezhadarya, Jimmy Ba:
Dataset Distillation using Neural Feature Regression. - Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, Jure Leskovec:
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time. - Bokun Wang, Mher Safaryan, Peter Richtárik:
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. - Shengding Hu, Zhen Zhang, Ning Ding, Yadao Wang, Yasheng Wang, Zhiyuan Liu, Maosong Sun:
Sparse Structure Search for Delta Tuning. - Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth Daly, Moninder Singh:
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. - Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long:
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting. - Anthony Corso, Sydney M. Katz, Craig Innes, Xin Du, Subramanian Ramamoorthy, Mykel J. Kochenderfer:
Risk-Driven Design of Perception Systems. - Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang:
A Simple Approach to Automated Spectral Clustering. - Ben Tu, Axel Gandy, Nikolas Kantas, Behrang Shafei:
Joint Entropy Search for Multi-Objective Bayesian Optimization. - Josh Gardner, Zoran Popovic, Ludwig Schmidt:
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. - Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang:
Robustness to Unbounded Smoothness of Generalized SignSGD. - Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu, Chao Xu, Yunhe Wang:
GhostNetV2: Enhance Cheap Operation with Long-Range Attention. - Zixuan Wang, Zhouzi Li, Jian Li:
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability. - Jiangxin Sun, Chunyu Wang, Huang Hu, Hanjiang Lai, Zhi Jin, Jian-Fang Hu:
You Never Stop Dancing: Non-freezing Dance Generation via Bank-constrained Manifold Projection. - Caleb Bugg, Chen Chen, Anil Aswani:
Nonnegative Tensor Completion via Integer Optimization. - Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis:
LION: Latent Point Diffusion Models for 3D Shape Generation. - Jun Wu, Jingrui He, Sheng Wang, Kaiyu Guan, Elizabeth A. Ainsworth:
Distribution-Informed Neural Networks for Domain Adaptation Regression. - Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh:
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation. - Mazda Moayeri, Sahil Singla, Soheil Feizi:
Hard ImageNet: Segmentations for Objects with Strong Spurious Cues. - Zhan Tong, Yibing Song, Jue Wang, Limin Wang:
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training. - Samarth Gupta, Saurabh Amin:
Scalable design of Error-Correcting Output Codes using Discrete Optimization with Graph Coloring. - Fredrik Hellström, Giuseppe Durisi:
A New Family of Generalization Bounds Using Samplewise Evaluated CMI. - Siliang Zeng, Chenliang Li, Alfredo García, Mingyi Hong:
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees. - Milton Llera Montero, Jeffrey S. Bowers, Rui Ponte Costa, Casimir J. H. Ludwig, Gaurav Malhotra:
Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation. - Jinyuan Jia, Wenjie Qu, Neil Zhenqiang Gong:
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples. - Florian Graf, Sebastian Zeng, Bastian Rieck, Marc Niethammer, Roland Kwitt:
On Measuring Excess Capacity in Neural Networks. - Mohamad Rida Rammal, Alessandro Achille, Aditya Golatkar, Suhas N. Diggavi, Stefano Soatto:
On Leave-One-Out Conditional Mutual Information For Generalization. - Qijia Jiang:
Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings. - Yaxin Xiao, Qingqing Ye, Haibo Hu, Huadi Zheng, Chengfang Fang, Jie Shi:
MExMI: Pool-based Active Model Extraction Crossover Membership Inference. - Yue Bai, Huan Wang, Xu Ma, Yitian Zhang, Zhiqiang Tao, Yun Fu:
Parameter-Efficient Masking Networks. - Sina Däubener, Asja Fischer:
How Sampling Impacts the Robustness of Stochastic Neural Networks. - Ling Liang, Kaidi Xu, Xing Hu, Lei Deng, Yuan Xie:
Toward Robust Spiking Neural Network Against Adversarial Perturbation. - Yemin Yu, Ying Wei, Kun Kuang, Zhengxing Huang, Huaxiu Yao, Fei Wu:
GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy. - Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, François Charton:
End-to-end Symbolic Regression with Transformers. - Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs:
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? - Jing Liu, Zizheng Pan, Haoyu He, Jianfei Cai, Bohan Zhuang:
EcoFormer: Energy-Saving Attention with Linear Complexity. - Huaxiu Yao, Caroline Choi, Bochuan Cao, Yoonho Lee, Pang Wei Koh, Chelsea Finn:
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time. - Tian-Zuo Wang, Tian Qin, Zhi-Hua Zhou:
Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge. - Zongkai Liu, Chao Yu, Yaodong Yang, Peng Sun, Zifan Wu, Yuan Li:
A Unified Diversity Measure for Multiagent Reinforcement Learning. - Yongming Rao, Wenliang Zhao, Yansong Tang, Jie Zhou, Ser-Nam Lim, Jiwen Lu:
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions. - Shasha Jin, Vasundhara Komaragiri, Tahrima Rahman, Vibhav Gogate:
Learning Tractable Probabilistic Models from Inconsistent Local Estimates. - Donghyeon Baek, Youngmin Oh, Sanghoon Lee, Junghyup Lee, Bumsub Ham:
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation. - Junpei Komiyama, Taira Tsuchiya, Junya Honda:
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification. - Ramana Sundararaman, Riccardo Marin, Emanuele Rodolà, Maks Ovsjanikov:
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching. - Tingting Liang, Hongwei Xie, Kaicheng Yu, Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Tao Tang, Bing Wang, Zhi Tang:
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework. - Gaurav Arya, Moritz Schauer, Frank Schäfer, Christopher Rackauckas:
Automatic Differentiation of Programs with Discrete Randomness. - Zonghan Yang, Tianyu Pang, Yang Liu:
A Closer Look at the Adversarial Robustness of Deep Equilibrium Models. - Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Near-Optimal Private and Scalable $k$-Clustering. - Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Luis Garcia, Christophe Hauser, Xiang Ren:
NS3: Neuro-symbolic Semantic Code Search. - Xili Dai, Mingyang Li, Pengyuan Zhai, Shengbang Tong, Xingjian Gao, Shao-Lun Huang, Zhihui Zhu, Chong You, Yi Ma:
Revisiting Sparse Convolutional Model for Visual Recognition. - Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Nitesh B. Gundavarapu, Alex Lamb, Nan Rosemary Ke, Yoshua Bengio:
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning. - Fan Chen, Junyu Zhang, Zaiwen Wen:
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP. - Jiawei Shao, Yuchang Sun, Songze Li, Jun Zhang:
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing. - Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Chao Shen:
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning. - Yuanze Lin, Yujia Xie, Dongdong Chen, Yichong Xu, Chenguang Zhu, Lu Yuan:
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering. - Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai:
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. - Dongxu Zhang, Michael Boratko, Cameron Musco, Andrew McCallum:
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings. - Xuanyuan Luo, Bei Luo, Jian Li:
Generalization Bounds for Gradient Methods via Discrete and Continuous Prior. - Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao:
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. - Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Memory Efficient Continual Learning with Transformers. - Yizhou Zhang, Defu Cao, Yan Liu:
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media. - Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao:
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation. - Lucas Maystre, Daniel Russo:
Temporally-Consistent Survival Analysis. - S. P. Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang:
Symbolic Distillation for Learned TCP Congestion Control. - Hugues Van Assel, Thibault Espinasse, Julien Chiquet, Franck Picard:
A Probabilistic Graph Coupling View of Dimension Reduction. - Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. - David A. R. Robin, Kevin Scaman, Marc Lelarge:
Convergence beyond the over-parameterized regime using Rayleigh quotients. - Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. - Xiaotong Yuan, Ping Li:
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond. - Antoine Salmona, Valentin De Bortoli, Julie Delon, Agnès Desolneux:
Can Push-forward Generative Models Fit Multimodal Distributions? - Samuel Pinilla, Tingting Mu, Neil Bourne, Jeyan Thiyagalingam:
Improved Imaging by Invex Regularizers with Global Optima Guarantees. - Mufan (Bill) Li, Mihai Nica, Daniel M. Roy:
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization. - Yizhen Zheng, Shirui Pan, Vincent C. S. Lee, Yu Zheng, Philip S. Yu:
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination. - Alexandre Ramé, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, Matthieu Cord:
Diverse Weight Averaging for Out-of-Distribution Generalization. - Yang Ni:
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. - Lokesh Boominathan, Xaq Pitkow:
Phase transitions in when feedback is useful. - Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran:
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions. - Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham:
Posterior and Computational Uncertainty in Gaussian Processes. - Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne:
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. - Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf:
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. - Alexandra Senderovich, Ekaterina Bulatova, Anton Obukhov, Maxim V. Rakhuba:
Towards Practical Control of Singular Values of Convolutional Layers. - Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas, Zheng Li, Volodymyr Kuleshov:
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies. - Tim De Ryck, Siddhartha Mishra:
Generic bounds on the approximation error for physics-informed (and) operator learning. - Saikiran Bulusu, Geethu Joseph, Mustafa Cenk Gursoy, Pramod K. Varshney:
Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers. - Rongjie Huang, Yi Ren, Jinglin Liu, Chenye Cui, Zhou Zhao:
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech. - Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon:
On Non-Linear operators for Geometric Deep Learning. - Hoang Tran, Ashok Cutkosky:
Momentum Aggregation for Private Non-convex ERM. - Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du:
Learning in Congestion Games with Bandit Feedback. - Jie-Jing Shao, Lan-Zhe Guo, Xiaowen Yang, Yufeng Li:
LOG: Active Model Adaptation for Label-Efficient OOD Generalization. - Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, Ling Shao:
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds. - Osama A. Hanna, Lin Yang, Christina Fragouli:
Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context. - Dingwen Kong, Lin Yang:
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning. - Aydar Bulatov, Yuri Kuratov, Mikhail Burtsev:
Recurrent Memory Transformer. - Hiroki Yanagisawa, Kohei Miyaguchi, Takayuki Katsuki:
Hierarchical Lattice Layer for Partially Monotone Neural Networks. - De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, Tongliang Liu:
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization. - Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L. Zhang, He He:
SeqPATE: Differentially Private Text Generation via Knowledge Distillation. - Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu:
Bayesian Persuasion for Algorithmic Recourse. - Jonathan Brophy, Daniel Lowd:
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees. - Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou, Tao Qin, Dongsheng Li:
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling. - Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer:
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. - Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew Beam:
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. - Junchi Yang, Xiang Li, Niao He:
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. - Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers:
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. - Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh:
Are AlphaZero-like Agents Robust to Adversarial Perturbations? - Yanze Wu, Xintao Wang, Gen Li, Ying Shan:
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos. - Amnon Geifman, Meirav Galun, David Jacobs, Ronen Basri:
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels. - Yatong Chen, Reilly Raab, Jialu Wang, Yang Liu:
Fairness Transferability Subject to Bounded Distribution Shift. - Yann Dubois, Stefano Ermon, Tatsunori B. Hashimoto, Percy Liang:
Improving Self-Supervised Learning by Characterizing Idealized Representations. - Jonas M. Mikhaeil, Zahra Monfared, Daniel Durstewitz:
On the difficulty of learning chaotic dynamics with RNNs. - Shangkun Sun, Yuanqi Chen, Yu Zhu, Guodong Guo, Ge Li:
SKFlow: Learning Optical Flow with Super Kernels. - Zhiwei Xu, Dapeng Li, Bin Zhang, Yuan Zhan, Yunpeng Bai, Guoliang Fan:
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning. - Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang:
End-to-end Stochastic Optimization with Energy-based Model. - Siwei Wang, Benjamin Hoshal, Elizabeth de Laittre, Thierry Mora, Michael Berry, Stephanie E. Palmer:
Learning low-dimensional generalizable natural features from retina using a U-net. - Yi-Shan Wu, Yevgeny Seldin:
Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables. - Yubo Zhuang, Xiaohui Chen, Yun Yang:
Wasserstein $K$-means for clustering probability distributions. - Matteo Turchetta, Luca Corinzia, Scott Sussex, Amanda Burton, Juan Herrera, Ioannis Athanasiadis, Joachim M. Buhmann, Andreas Krause:
Learning Long-Term Crop Management Strategies with CyclesGym. - Yi Liu, Ke Sun, Bei Jiang, Linglong Kong:
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. - Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi:
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. - Yan Dai, Haipeng Luo, Liyu Chen:
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback. - Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi:
Improving Multi-Task Generalization via Regularizing Spurious Correlation. - Lingjiao Chen, Matei Zaharia, James Y. Zou:
Estimating and Explaining Model Performance When Both Covariates and Labels Shift. - Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, Yu Xu, Xiaohu Qie:
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems. - Carl Hvarfner, Frank Hutter, Luigi Nardi:
Joint Entropy Search For Maximally-Informed Bayesian Optimization. - Rui Lu, Andrew Zhao, Simon S. Du, Gao Huang:
Provable General Function Class Representation Learning in Multitask Bandits and MDP. - Miao Zhang, Li Wang, David Campos, Wei Huang, Chenjuan Guo, Bin Yang:
Weighted Mutual Learning with Diversity-Driven Model Compression. - Daesol Cho, Dongseok Shim, H. Jin Kim:
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning. - Can Yaras, Peng Wang, Zhihui Zhu, Laura Balzano, Qing Qu:
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. - Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang:
Conformalized Fairness via Quantile Regression. - Peng Zhao, Yan-Feng Xie, Lijun Zhang, Zhi-Hua Zhou:
Efficient Methods for Non-stationary Online Learning. - Leonard Papenmeier, Luigi Nardi, Matthias Poloczek:
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces. - Huiping Zhuang, Zhenyu Weng, Hongxin Wei, Renchunzi Xie, Kar-Ann Toh, Zhiping Lin:
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection. - Shaocong Dong, Lihe Ding, Haiyang Wang, Tingfa Xu, Xinli Xu, Jie Wang, Ziyang Bian, Ying Wang, Jianan Li:
MsSVT: Mixed-scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds. - Richard D. P. Grumitt, Biwei Dai, Uros Seljak:
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference. - Noel Loo, Ramin M. Hasani, Alexander Amini, Daniela Rus:
Evolution of Neural Tangent Kernels under Benign and Adversarial Training. - Denizalp Goktas, Sadie Zhao, Amy Greenwald:
Zero-Sum Stochastic Stackelberg Games. - Stefan Larson, Gordon Lim, Yutong Ai, David Kuang, Kevin Leach:
Evaluating Out-of-Distribution Performance on Document Image Classifiers. - Heng Lian:
Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space. - Nikoli Dryden, Torsten Hoefler:
Spatial Mixture-of-Experts. - Huafeng Liu, Liping Jing:
Amortized Mixing Coupling Processes for Clustering. - Dian Qin, Haishuai Wang, Zhe Liu, Hongjia Xu, Sheng Zhou, Jiajun Bu:
Hilbert Distillation for Cross-Dimensionality Networks. - Qiwen Cui, Simon S. Du:
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus. - Saeed Masoudian, Julian Zimmert, Yevgeny Seldin:
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback. - Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris S. Papailiopoulos, Kangwook Lee:
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks. - Erik Jones, Jacob Steinhardt:
Capturing Failures of Large Language Models via Human Cognitive Biases. - Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty:
Template based Graph Neural Network with Optimal Transport Distances. - Lirong Wu, Haitao Lin, Yufei Huang, Stan Z. Li:
Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks. - Haoyang Li, Ziwei Zhang, Xin Wang, Wenwu Zhu:
Learning Invariant Graph Representations for Out-of-Distribution Generalization. - Rémi Leluc, François Portier, Johan Segers, Aigerim Zhuman:
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling. - Enmao Diao, Jie Ding, Vahid Tarokh:
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. - Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz, Bernhard Schölkopf:
Direct Advantage Estimation. - Hua Wei, Jingxiao Chen, Xiyang Ji, Hongyang Qin, Minwen Deng, Siqin Li, Liang Wang, Weinan Zhang, Yong Yu, Liu Lin, Lanxiao Huang, Deheng Ye, Qiang Fu, Wei Yang:
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning. - Charles Godfrey, Davis Brown, Tegan Emerson, Henry Kvinge:
On the Symmetries of Deep Learning Models and their Internal Representations. - Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc V. Le, Da Huang:
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets. - Ruoxi Jiang, Rebecca Willett:
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification. - Hao Li, Jingkuan Song, Lianli Gao, Pengpeng Zeng, Haonan Zhang, Gongfu Li:
A Differentiable Semantic Metric Approximation in Probabilistic Embedding for Cross-Modal Retrieval. - Tian Yu Liu, Yu Yang, Baharan Mirzasoleiman:
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack. - Ziyue Jiang, Su Zhe, Zhou Zhao, Qian Yang, Yi Ren, Jinglin Liu:
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech. - Daniele Zambon, Cesare Alippi:
AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs. - Fermín Travi, Gonzalo Ruarte, Gastón Bujia, Juan E. Kamienkowski:
ViSioNS: Visual Search in Natural Scenes Benchmark. - James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, Peter Stone:
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents. - Yanming Wan, Jiayuan Mao, Josh Tenenbaum:
HandMeThat: Human-Robot Communication in Physical and Social Environments. - Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward W. Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji:
Task-Agnostic Graph Explanations. - Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve:
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. - Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot:
Dataset Inference for Self-Supervised Models. - Colin Wei, Yining Chen, Tengyu Ma:
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers. - Yudong Chen, Sen Wang, Jiajun Liu, Xuwei Xu, Frank de Hoog, Zi Huang:
Improved Feature Distillation via Projector Ensemble. - Ruoxi Sun, Hanjun Dai, Adams Wei Yu:
Does GNN Pretraining Help Molecular Representation? - Igor Santesteban, Miguel A. Otaduy, Nils Thuerey, Dan Casas:
ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On. - Mohit Prabhushankar, Ghassan AlRegib:
Introspective Learning : A Two-Stage approach for Inference in Neural Networks. - Christoffer Riis, Francisco Antunes, Frederik Boe Hüttel, Carlos Lima Azevedo, Francisco Pereira:
Bayesian Active Learning with Fully Bayesian Gaussian Processes. - Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu:
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance. - Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, Pawan Kumar Mudigonda:
In Defense of the Unitary Scalarization for Deep Multi-Task Learning. - Chenjian Gao, Tongda Xu, Dailan He, Yan Wang, Hongwei Qin:
Flexible Neural Image Compression via Code Editing. - Xiao-Yang Liu, Zechu (Steven) Li, Xiaodong Wang:
Homomorphic Matrix Completion. - Weirui Ye, Pieter Abbeel, Yang Gao:
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions. - Christian Horvat, Jean-Pascal Pfister:
Intrinsic dimensionality estimation using Normalizing Flows. - Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian D. Ziebart, Kevin Gimpel:
Moment Distributionally Robust Tree Structured Prediction. - Junwen Yang, Vincent Y. F. Tan:
Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits. - Muralidhar Andoorveedu, Zhanda Zhu, Bojian Zheng, Gennady Pekhimenko:
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction. - Muhammad Ferjad Naeem, Yongqin Xian, Luc Van Gool, Federico Tombari:
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification. - Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. - Tao Yang, Jinghao Deng, Xiaojun Quan, Qifan Wang, Shaoliang Nie:
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning. - Tianfan Fu, Wenhao Gao, Connor W. Coley, Jimeng Sun:
Reinforced Genetic Algorithm for Structure-based Drug Design. - Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, Mingyuan Zhou:
A Variational Edge Partition Model for Supervised Graph Representation Learning. - Yu Cao, Eric Vanden-Eijnden:
Learning Optimal Flows for Non-Equilibrium Importance Sampling. - Victor Feitosa Souza, Ferdinando Cicalese, Eduardo Sany Laber, Marco Molinaro:
Decision Trees with Short Explainable Rules. - Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar:
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. - Dari Kimanius, Kiarash Jamali, Sjors H. W. Scheres:
Sparse Fourier Backpropagation in Cryo-EM Reconstruction. - Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg:
Modular Flows: Differential Molecular Generation. - Hossein Esfandiari, Vahab Mirrokni, Jon Schneider:
Anonymous Bandits for Multi-User Systems. - Yizhou Wang, Meilin Chen, Shixiang Tang, Feng Zhu, Haiyang Yang, Lei Bai, Rui Zhao, Yunfeng Yan, Donglian Qi, Wanli Ouyang:
Unsupervised Object Detection Pretraining with Joint Object Priors Generation and Detector Learning. - Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk:
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. - Yabo Xiao, Kai Su, Xiaojuan Wang, Dongdong Yu, Lei Jin, Mingshu He, Zehuan Yuan:
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query. - Thomas Carta, Pierre-Yves Oudeyer, Olivier Sigaud, Sylvain Lamprier:
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL. - Piera Riccio, Bill Psomas, Francesco Galati, Francisco Escolano, Thomas Hofmann, Nuria Oliver:
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters. - Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel:
Improving Policy Learning via Language Dynamics Distillation. - Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. - Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford:
Interaction-Grounded Learning with Action-Inclusive Feedback. - Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland:
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels. - Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Dieterich Lawson, Botao Hao, Brendan O'Donoghue, Benjamin Van Roy:
The Neural Testbed: Evaluating Joint Predictions. - Stefan Stojanov, Anh Thai, Zixuan Huang, James M. Rehg:
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization. - Yaoguang Zhai, Sicun Gao:
Monte Carlo Tree Descent for Black-Box Optimization. - Yongchang Hao, Yuxin Liu, Lili Mou:
Teacher Forcing Recovers Reward Functions for Text Generation. - Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel:
Masked Autoencoding for Scalable and Generalizable Decision Making. - Jifeng Hu, Yanchao Sun, Hechang Chen, Sili Huang, Haiyin Piao, Yi Chang, Lichao Sun:
Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning. - Oren Mangoubi, Nisheeth K. Vishnoi:
Sampling from Log-Concave Distributions with Infinity-Distance Guarantees. - Jiaxin Zhang, Yashar Moshfeghi:
ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler. - Qu Yang, Jibin Wu, Malu Zhang, Yansong Chua, Xinchao Wang, Haizhou Li:
Training Spiking Neural Networks with Local Tandem Learning. - Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin:
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting. - Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang:
Differentially Private Linear Sketches: Efficient Implementations and Applications. - Fukun Yin, Wen Liu, Zilong Huang, Pei Cheng, Tao Chen, Gang Yu:
Coordinates Are NOT Lonely - Codebook Prior Helps Implicit Neural 3D representations. - Subin Kim, Sihyun Yu, Jaeho Lee, Jinwoo Shin:
Scalable Neural Video Representations with Learnable Positional Features. - Nianqiao Ju, Jordan Awan, Ruobin Gong, Vinayak Rao:
Data Augmentation MCMC for Bayesian Inference from Privatized Data. - Nikhil X. Bhattasali, Anthony M. Zador, Tatiana A. Engel:
Neural Circuit Architectural Priors for Embodied Control. - Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy:
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. - Thai Hung Le, Thommen Karimpanal George, Majid Abdolshah, Dung Nguyen, Kien Do, Sunil Gupta, Svetha Venkatesh:
Learning to Constrain Policy Optimization with Virtual Trust Region. - Davin Choo, Kirankumar Shiragur, Arnab Bhattacharyya:
Verification and search algorithms for causal DAGs. - Jiayun Zheng, Maggie Makar:
Causally motivated multi-shortcut identification and removal. - Joshua Albrecht, Abraham J. Fetterman, Bryden Fogelman, Ellie Kitanidis, Bartosz Wróblewski, Nicole Seo, Michael Rosenthal, Maksis Knutins, Zack Polizzi, James Simon, Kanjun Qiu:
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds. - Wenguan Wang, James Liang, Dongfang Liu:
Learning Equivariant Segmentation with Instance-Unique Querying. - Yi-Ling Qiao, Alexander Gao, Ming C. Lin:
NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos. - Mengda Yang, Ziang Li, Juan Wang, Hongxin Hu, Ao Ren, Xiaoyang Xu, Wenzhe Yi:
Measuring Data Reconstruction Defenses in Collaborative Inference Systems. - Xiaoxing Wang, Wenxuan Guo, Jianlin Su, Xiaokang Yang, Junchi Yan:
ZARTS: On Zero-order Optimization for Neural Architecture Search. - Pau de Jorge Aranda, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania:
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training. - Maying Shen, Hongxu Yin, Pavlo Molchanov, Lei Mao, Jianna Liu, José M. Álvarez:
Structural Pruning via Latency-Saliency Knapsack. - Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. - Yucheng Shi, Yahong Han, Yu-an Tan, Xiaohui Kuang:
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal. - Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren:
EfficientFormer: Vision Transformers at MobileNet Speed. - Axel Laborieux, Friedemann Zenke:
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations. - Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, Junchi Yan:
Learning Substructure Invariance for Out-of-Distribution Molecular Representations. - William Gaviria Rojas, Sudnya Frederick Diamos, Keertan Kini, David Kanter, Vijay Janapa Reddi, Cody Coleman:
The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World. - Yi-Lin Sung, Jaemin Cho, Mohit Bansal:
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning. - Xikun Zhang, Dongjin Song, Dacheng Tao:
CGLB: Benchmark Tasks for Continual Graph Learning. - Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, Huazhe Xu:
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning. - Axel Levy, Gordon Wetzstein, Julien N. P. Martel, Frédéric Poitevin, Ellen D. Zhong:
Amortized Inference for Heterogeneous Reconstruction in Cryo-EM. - Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic:
RKHS-SHAP: Shapley Values for Kernel Methods. - Alakh Desai, Tz-Ying Wu, Subarna Tripathi, Nuno Vasconcelos:
Single-Stage Visual Relationship Learning using Conditional Queries. - Joachim Hyam Rubinstein, Benjamin I. P. Rubinstein:
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes. - Fabian Mentzer, George Toderici, David Minnen, Sergi Caelles, Sung Jin Hwang, Mario Lucic, Eirikur Agustsson:
VCT: A Video Compression Transformer. - Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf:
Amortized Inference for Causal Structure Learning. - Shuwen Qiu, Sirui Xie, Lifeng Fan, Tao Gao, Jungseock Joo, Song-Chun Zhu, Yixin Zhu:
Emergent Graphical Conventions in a Visual Communication Game. - Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li:
Repairing Neural Networks by Leaving the Right Past Behind. - Jooyoung Lee, Seyoon Jeong, Munchurl Kim:
Selective compression learning of latent representations for variable-rate image compression. - Zejiang Shen, Kyle Lo, Lauren Yu, Nathan Dahlberg, Margo Schlanger, Doug Downey:
Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities. - Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini:
Increasing Confidence in Adversarial Robustness Evaluations. - Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett:
Local Bayesian optimization via maximizing probability of descent. - Da Ju, Stephen Roller, Sainbayar Sukhbaatar, Jason Weston:
Staircase Attention for Recurrent Processing of Sequences. - Chi Zhang, Karthika Mohan, Judea Pearl:
Causal Inference with Non-IID Data using Linear Graphical Models. - Han Qi, Yi Su, Aviral Kumar, Sergey Levine:
Data-Driven Offline Decision-Making via Invariant Representation Learning. - Yiming Li, Yang Bai, Yong Jiang, Yong Yang, Shu-Tao Xia, Bo Li:
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection. - Sangho Lim, Eun-Gyeol Oh, Hongseok Yang:
Learning Symmetric Rules with SATNet. - Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr:
The Privacy Onion Effect: Memorization is Relative. - Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai, Yutaka Matsuo:
Langevin Autoencoders for Learning Deep Latent Variable Models. - Jiarui Gan, Rupak Majumdar, Adish Singla, Goran Radanovic:
Envy-free Policy Teaching to Multiple Agents. - Arnob Ghosh, Xingyu Zhou, Ness B. Shroff:
Provably Efficient Model-Free Constrained RL with Linear Function Approximation. - Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro:
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. - Yuqing Kong, Yunqi Li, Yubo Zhang, Zhihuan Huang, Jinzhao Wu:
Eliciting Thinking Hierarchy without a Prior. - Alexandros Psomas, Paritosh Verma:
Fair and Efficient Allocations Without Obvious Manipulations. - Yuqiao Liu, Yehui Tang, Zeqiong Lv, Yunhe Wang, Yanan Sun:
Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor. - Daniel Melcer, Christopher Amato, Stavros Tripakis:
Shield Decentralization for Safe Multi-Agent Reinforcement Learning. - Ozan Sener, Vladlen Koltun:
Domain Generalization without Excess Empirical Risk. - Vineel Pratap, Awni Hannun, Gabriel Synnaeve, Ronan Collobert:
Star Temporal Classification: Sequence Modeling with Partially Labeled Data. - Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, Zhangyang Wang:
Signal Processing for Implicit Neural Representations. - Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andrés Codas, Mark Encarnación, Shuvendu K. Lahiri, Madanlal Musuvathi, Jianfeng Gao:
Fault-Aware Neural Code Rankers. - Zijie Zhang, Yang Zhou, Xin Zhao, Tianshi Che, Lingjuan Lyu:
Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization. - Huan Wang, Suhas Lohit, Michael N. Jones, Yun Fu:
What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective. - Martin Wistuba, Arlind Kadra, Josif Grabocka:
Supervising the Multi-Fidelity Race of Hyperparameter Configurations. - Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan:
Streaming Radiance Fields for 3D Video Synthesis. - Xu Zhang, Zhenyuan Yuan, Minghui Zhu:
Byzantine-tolerant federated Gaussian process regression for streaming data. - Sunghwan Hong, Jisu Nam, Seokju Cho, Susung Hong, Sangryul Jeon, Dongbo Min, Seungryong Kim:
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence. - Shubham Gupta, Ambedkar Dukkipati:
Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions. - Mengwei Ren, Neel Dey, Martin Styner, Kelly N. Botteron, Guido Gerig:
Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis. - Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Mingyuan Wang:
Overparameterization from Computational Constraints. - Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang:
A Unifying Framework of Off-Policy General Value Function Evaluation. - Ruida Zhou, Tao Liu, Dileep Kalathil, P. R. Kumar, Chao Tian:
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning. - Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, Orhan Firat:
Do Current Multi-Task Optimization Methods in Deep Learning Even Help? - Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, Yi Yang:
TAP-Vid: A Benchmark for Tracking Any Point in a Video. - Yizhou Zhao, Zhenyang Li, Xun Guo, Yan Lu:
Alignment-guided Temporal Attention for Video Action Recognition. - Zohar Rimon, Aviv Tamar, Gilad Adler:
Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach. - Junting Dong, Qi Fang, Yudong Guo, Sida Peng, Qing Shuai, Xiaowei Zhou, Hujun Bao:
TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies. - Seungjae Lee, Jigang Kim, Inkyu Jang, H. Jin Kim:
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning. - Devanshu Agrawal, James Ostrowski:
A Classification of $G$-invariant Shallow Neural Networks. - Binh T. Nguyen, Bertrand Thirion, Sylvain Arlot:
A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension. - Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan:
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources. - Zicheng Zhang, Yinglu Liu, Congying Han, Tiande Guo, Ting Yao, Tao Mei:
Generalized One-shot Domain Adaptation of Generative Adversarial Networks. - Dídac Surís, Carl Vondrick:
Representing Spatial Trajectories as Distributions. - Ahmad Darkhalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard E. L. Higgins, Sanja Fidler, David Fouhey, Dima Damen:
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations. - Kelsey R. Allen, Tatiana Lopez-Guevara, Kimberly L. Stachenfeld, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Jessica B. Hamrick, Tobias Pfaff:
Inverse Design for Fluid-Structure Interactions using Graph Network Simulators. - Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani, Daniel Cremers:
What Makes Graph Neural Networks Miscalibrated? - Kyungsu Lee, Jaeseung Yang, Haeyun Lee, Jae Youn Hwang:
Stochastic Adaptive Activation Function. - Joseph DelPreto, Chao Liu, Yiyue Luo, Michael Foshey, Yunzhu Li, Antonio Torralba, Wojciech Matusik, Daniela Rus:
ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment. - Anastasia Antsiferova, Sergey Lavrushkin, Maksim Smirnov, Aleksandr Gushchin, Dmitriy S. Vatolin, Dmitriy L. Kulikov:
Video compression dataset and benchmark of learning-based video-quality metrics. - Avinandan Bose, Arunesh Sinha, Tien Mai:
Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees. - Shizhen Zhao, Xiaojuan Qi:
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection. - C. J. Argue, Alan M. Frieze, Anupam Gupta, Christopher Seiler:
Learning from a Sample in Online Algorithms. - Dominik Peters, Ariel D. Procaccia, David Zhu:
Robust Rent Division. - Noel Loo, Ramin M. Hasani, Alexander Amini, Daniela Rus:
Efficient Dataset Distillation using Random Feature Approximation. - Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit:
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. - James Enouen, Yan Liu:
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection. - Lesi Chen, Boyuan Yao, Luo Luo:
Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition. - Alexander Korotin, Alexander Kolesov, Evgeny Burnaev:
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport? - Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. - Jiaqi Xi, Jonathan Niles-Weed:
Distributional Convergence of the Sliced Wasserstein Process. - Zhenyu Wang, Hao Luo, Pichao Wang, Feng Ding, Fan Wang, Hao Li:
VTC-LFC: Vision Transformer Compression with Low-Frequency Components. - Han Shao, Omar Montasser, Avrim Blum:
A Theory of PAC Learnability under Transformation Invariances. - Nikita Balagansky, Daniil Gavrilov:
PALBERT: Teaching ALBERT to Ponder. - Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov:
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. - Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan:
Analyzing Data-Centric Properties for Graph Contrastive Learning. - José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar:
Learning Bipartite Graphs: Heavy Tails and Multiple Components. - Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry P. Vetrov:
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. - Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin:
Exploring the Whole Rashomon Set of Sparse Decision Trees. - Dongki Kim, Jinheon Baek, Sung Ju Hwang:
Graph Self-supervised Learning with Accurate Discrepancy Learning. - Yuanbiao Gou, Peng Hu, Jiancheng Lv, Joey Tianyi Zhou, Xi Peng:
Multi-Scale Adaptive Network for Single Image Denoising. - Weishun Zhong, Ben Sorscher, Daniel Lee, Haim Sompolinsky:
A theory of weight distribution-constrained learning. - Chi Zhang, Wei Yin, Billzb Wang, Gang Yu, Bin Fu, Chunhua Shen:
Hierarchical Normalization for Robust Monocular Depth Estimation. - Naigang Wang, Chi-Chun (Charlie) Liu, Swagath Venkataramani, Sanchari Sen, Chia-Yu Chen, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Leland Chang:
Deep Compression of Pre-trained Transformer Models. - Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy. - Ali Shahin Shamsabadi, Mohammad Yaghini, Natalie Dullerud, Sierra Calanda Wyllie, Ulrich Aïvodji, Aisha Alaagib, Sébastien Gambs, Nicolas Papernot:
Washing The Unwashable : On The (Im)possibility of Fairwashing Detection. - Clémence Réda, Sattar Vakili, Emilie Kaufmann:
Near-Optimal Collaborative Learning in Bandits. - Daniel Alabi, Salil P. Vadhan:
Hypothesis Testing for Differentially Private Linear Regression. - Chenyang Wu, Tianci Li, Zongzhang Zhang, Yang Yu:
Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning. - Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim:
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers. - Zhen Qin, Alexander Lidiak, Zhexuan Gong, Gongguo Tang, Michael B. Wakin, Zhihui Zhu:
Error Analysis of Tensor-Train Cross Approximation. - Yihang Gao, Man-Chung Yue, Michael Ng:
Approximate Secular Equations for the Cubic Regularization Subproblem. - Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting:
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. - Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. - Gang Li, Heliang Zheng, Daqing Liu, Chaoyue Wang, Bing Su, Changwen Zheng:
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders. - Zechun Liu, Barlas Oguz, Aasish Pappu, Lin Xiao, Scott Yih, Meng Li, Raghuraman Krishnamoorthi, Yashar Mehdad:
BiT: Robustly Binarized Multi-distilled Transformer. - Jinyu Cai, Jicong Fan:
Perturbation Learning Based Anomaly Detection. - Dongsheng Wang, Yishi Xu, Miaoge Li, Zhibin Duan, Chaojie Wang, Bo Chen, Mingyuan Zhou:
Knowledge-Aware Bayesian Deep Topic Model. - Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak, Seong Joon Oh, Damien Teney, Jin-Hwa Kim, Byoung-Tak Zhang:
SelecMix: Debiased Learning by Contradicting-pair Sampling. - Aayush Jung Rana, Yogesh S. Rawat:
Are all Frames Equal? Active Sparse Labeling for Video Action Detection. - Quanliang Wu, Huajun Liu:
Unsupervised Domain Adaptation for Semantic Segmentation using Depth Distribution. - Ziyi Wang, Xumin Yu, Yongming Rao, Jie Zhou, Jiwen Lu:
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting. - Nataniel Ruiz, Sarah A. Bargal, Cihang Xie, Kate Saenko, Stan Sclaroff:
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing. - Amir Feder, Guy Horowitz, Yoav Wald, Roi Reichart, Nir Rosenfeld:
In the Eye of the Beholder: Robust Prediction with Causal User Modeling. - Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet:
Variational inference via Wasserstein gradient flows. - Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Seth Lloyd:
projUNN: efficient method for training deep networks with unitary matrices. - Jonatha Anselmi, Bruno Gaujal, Louis-Sébastien Rebuffi:
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space. - Junwei Liang, Enwei Zhang, Jun Zhang, Chunhua Shen:
Multi-dataset Training of Transformers for Robust Action Recognition. - Xiu-Shen Wei, He-Yang Xu, Faen Zhang, Yuxin Peng, Wei Zhou:
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning. - Ladislav Rampásek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini:
Recipe for a General, Powerful, Scalable Graph Transformer. - Youngmin Oh, Donghyeon Baek, Bumsub Ham:
ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation. - Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. - Zizheng Pan, Jianfei Cai, Bohan Zhuang:
Fast Vision Transformers with HiLo Attention. - Billy Jin, Will Ma:
Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model. - Eshaan Nichani, Yu Bai, Jason D. Lee:
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. - Jinwoo Kim, Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong:
Pure Transformers are Powerful Graph Learners. - Huaibo Huang, Xiaoqiang Zhou, Ran He:
Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization. - Francesco Pinto, Harry Yang, Ser Nam Lim, Philip H. S. Torr, Puneet K. Dokania:
Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness. - Jiaqi Gu, Zhengqi Gao, Chenghao Feng, Hanqing Zhu, Ray T. Chen, Duane S. Boning, David Z. Pan:
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation. - Anirudh Rayas, Rajasekhar Anguluri, Gautam Dasarathy:
Learning the Structure of Large Networked Systems Obeying Conservation Laws. - Andrey Kuzmin, Mart van Baalen, Yuwei Ren, Markus Nagel, Jorn Peters, Tijmen Blankevoort:
FP8 Quantization: The Power of the Exponent. - Zhiying Lu, Hongtao Xie, Chuanbin Liu, Yongdong Zhang:
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets. - Dingfan Chen, Raouf Kerkouche, Mario Fritz:
Private Set Generation with Discriminative Information. - Dmitry Kovalev, Alexander V. Gasnikov:
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization. - Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, Jerry Zhu:
Provable Defense against Backdoor Policies in Reinforcement Learning. - Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, Dimitris Samaras:
Diffusion Models as Plug-and-Play Priors. - Zicheng Zhang, Yi Zhu, Jianzhuang Liu, Xiaodan Liang, Wei Ke:
CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation. - Ramtin Hosseini, Pengtao Xie:
Saliency-Aware Neural Architecture Search. - Hazal Koptagel, Oskar Kviman, Harald Melin, Negar Safinianaini, Jens Lagergren:
VaiPhy: a Variational Inference Based Algorithm for Phylogeny. - Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, Yunzhe Jia:
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal. - Annie S. Chen, Archit Sharma, Sergey Levine, Chelsea Finn:
You Only Live Once: Single-Life Reinforcement Learning. - Jiaxiang Tang, Xiaokang Chen, Jingbo Wang, Gang Zeng:
Compressible-composable NeRF via Rank-residual Decomposition. - Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill:
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. - Michelangelo Conserva, Paulo E. Rauber:
Hardness in Markov Decision Processes: Theory and Practice. - Zeren Shui, Daniel S. Karls, Mingjian Wen, Ilia A. Nikiforov, Ellad B. Tadmor, George Karypis:
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties. - Jayesh K. Gupta, Sai Vemprala, Ashish Kapoor:
Learning Modular Simulations for Homogeneous Systems. - Xinqi Wang, Qiwen Cui, Simon S. Du:
On Gap-dependent Bounds for Offline Reinforcement Learning. - Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Semi-Discrete Normalizing Flows through Differentiable Tessellation. - Ziwei Xu, Yogesh S. Rawat, Yongkang Wong, Mohan S. Kankanhalli, Mubarak Shah:
Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation. - Xutong Liu, Jinhang Zuo, Siwei Wang, Carlee Joe-Wong, John C. S. Lui, Wei Chen:
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms. - Yuren Mao, Yaobo Liang, Nan Duan, Haobo Wang, Kai Wang, Lu Chen, Yunjun Gao:
Less-forgetting Multi-lingual Fine-tuning. - Sizhe Chen, Zhehao Huang, Qinghua Tao, Yingwen Wu, Cihang Xie, Xiaolin Huang:
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks. - Zichang Liu, Benjamin Coleman, Tianyi Zhang, Anshumali Shrivastava:
Retaining Knowledge for Learning with Dynamic Definition. - Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, Wei Liang, Siyuan Huang:
HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes. - Xiang Gu, Yucheng Yang, Wei Zeng, Jian Sun, Zongben Xu:
Keypoint-Guided Optimal Transport with Applications in Heterogeneous Domain Adaptation. - Longtao Tang, Ying Zhou, Yu Yang:
Sequence-to-Set Generative Models. - Manish Prajapat, Matteo Turchetta, Melanie N. Zeilinger, Andreas Krause:
Near-Optimal Multi-Agent Learning for Safe Coverage Control. - Mengchu Li, Thomas Berrett, Yi Yu:
Network change point localisation under local differential privacy. - Arnab Bhattacharyya, Clément L. Canonne, Joy Qiping Yang:
Independence Testing for Bounded Degree Bayesian Networks. - Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond spectral gap: the role of the topology in decentralized learning. - Christopher Bamford, Minqi Jiang, Mikayel Samvelyan, Tim Rocktäschel:
GriddlyJS: A Web IDE for Reinforcement Learning. - Keqiang Yan, Yi Liu, Yuchao Lin, Shuiwang Ji:
Periodic Graph Transformers for Crystal Material Property Prediction. - Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar:
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. - Ceyuan Yang, Yujun Shen, Yinghao Xu, Deli Zhao, Bo Dai, Bolei Zhou:
Improving GANs with A Dynamic Discriminator. - Lin Chen, Zhixiang Wei, Xin Jin, Huaian Chen, Miao Zheng, Kai Chen, Yi Jin:
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation. - Yuxuan Yi, Ge Li, Yaowei Wang, Zongqing Lu:
Learning to Share in Networked Multi-Agent Reinforcement Learning. - Dariusz R. Kowalski, Dominik Pajak:
Scalable and Efficient Non-adaptive Deterministic Group Testing. - Xipeng Chen, Guangrun Wang, Dizhong Zhu, Xiaodan Liang, Philip H. S. Torr, Liang Lin:
Structure-Preserving 3D Garment Modeling with Neural Sewing Machines. - Tim Reichelt, Luke Ong, Thomas Rainforth:
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support. - Grigory Malinovsky, Kai Yi, Peter Richtárik:
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. - Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu:
DreamShard: Generalizable Embedding Table Placement for Recommender Systems. - Charlie Dickens, Justin Thaler, Daniel Ting:
Order-Invariant Cardinality Estimators Are Differentially Private. - Connor Z. Lin, Niloy J. Mitra, Gordon Wetzstein, Leonidas J. Guibas, Paul Guerrero:
NeuForm: Adaptive Overfitting for Neural Shape Editing. - Michael Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang:
Inductive Logical Query Answering in Knowledge Graphs. - Yichao Liang, Josh Tenenbaum, Tuan Anh Le, N. Siddharth:
Drawing out of Distribution with Neuro-Symbolic Generative Models. - Alireza Nasiri, Tristan Bepler:
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE. - Jing Tan, Xiaotong Zhao, Xintian Shi, Bin Kang, Limin Wang:
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points. - Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine:
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. - Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, Varun Jampani:
LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery. - Andreas Blattmann, Robin Rombach, Kaan Oktay, Jonas Müller, Björn Ommer:
Retrieval-Augmented Diffusion Models. - Radu Marinescu, Haifeng Qian, Alexander G. Gray, Debarun Bhattacharjya, Francisco Barahona, Tian Gao, Ryan Riegel, Pravinda Sahu:
Logical Credal Networks. - Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka:
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. - Gen Li, Yuejie Chi, Yuting Wei, Yuxin Chen:
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model. - Linhao Qu, Xiaoyuan Luo, Manning Wang, Zhijian Song:
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification. - Gecia Bravo Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andrés Muñoz Medina, Umar Syed:
Private and Communication-Efficient Algorithms for Entropy Estimation. - Weihan Cao, Yifan Zhang, Jianfei Gao, Anda Cheng, Ke Cheng, Jian Cheng:
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient. - Brandon Cui, Hengyuan Hu, Andrei Lupu, Samuel Sokota, Jakob N. Foerster:
Off-Team Learning. - Jian Liang, Chenfei Wu, Xiaowei Hu, Zhe Gan, Jianfeng Wang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan:
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis. - Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai Wu, Yong Liu, Chengjie Wang, Feng Zheng:
SoftPatch: Unsupervised Anomaly Detection with Noisy Data. - Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Jue Wang, Zhi-Quan Luo:
Stability Analysis and Generalization Bounds of Adversarial Training. - Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, Tat-Seng Chua:
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model. - Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman:
STaR: Bootstrapping Reasoning With Reasoning. - Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar:
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s. - Zhizhou Ren, Anji Liu, Yitao Liang, Jian Peng, Jianzhu Ma:
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation. - Kartik Ahuja, Jason S. Hartford, Yoshua Bengio:
Weakly Supervised Representation Learning with Sparse Perturbations. - Fabian Falck, Christopher Williams, Dominic Danks, George Deligiannidis, Christopher Yau, Chris C. Holmes, Arnaud Doucet, Matthew Willetts:
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. - Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. - Sheng Shen, Chunyuan Li, Xiaowei Hu, Yujia Xie, Jianwei Yang, Pengchuan Zhang, Zhe Gan, Lijuan Wang, Lu Yuan, Ce Liu, Kurt Keutzer, Trevor Darrell, Anna Rohrbach, Jianfeng Gao:
K-LITE: Learning Transferable Visual Models with External Knowledge. - Andrei Manolache, Florin Brad, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu:
VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web. - Gyubok Lee, Hyeonji Hwang, Seongsu Bae, Yeonsu Kwon, Woncheol Shin, Seongjun Yang, Minjoon Seo, Jong-Yeup Kim, Edward Choi:
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records. - Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. - Irina Cristali, Victor Veitch:
Using Embeddings for Causal Estimation of Peer Influence in Social Networks. - Shu Ding, Wei Wang:
Collaborative Learning by Detecting Collaboration Partners. - Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos:
Linear Label Ranking with Bounded Noise. - Patrick Dendorfer, Vladimir Yugay, Aljosa Osep, Laura Leal-Taixé:
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking? - Alexander Korotin, Vage Egiazarian, Lingxiao Li, Evgeny Burnaev:
Wasserstein Iterative Networks for Barycenter Estimation. - Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Identifiability of deep generative models without auxiliary information. - Andrei Atanov, Andrei Filatov, Teresa Yeo, Ajay Sohmshetty, Amir Zamir:
Task Discovery: Finding the Tasks that Neural Networks Generalize on. - Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, Tianwei Zhang:
Iron: Private Inference on Transformers. - Farnam Mansouri, Hans Simon, Adish Singla, Sandra Zilles:
On Batch Teaching with Sample Complexity Bounded by VCD. - Caio Kalil Lauand, Sean P. Meyn:
Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization. - Hanhan Zhou, Tian Lan, Vaneet Aggarwal:
PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning. - Wenhao Zhang, Ying Nian Wu, Si Wu:
Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors. - Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju:
OpenXAI: Towards a Transparent Evaluation of Model Explanations. - Idan Amir, Guy Azov, Tomer Koren, Roi Livni:
Better Best of Both Worlds Bounds for Bandits with Switching Costs. - Jian Yang, Kai Zhu, Kecheng Zheng, Yang Cao:
Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification. - Johan Larsson, Jonas Wallin:
The Hessian Screening Rule. - Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash:
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. - Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo:
Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning. - Yiyue Qian, Chunhui Zhang, Yiming Zhang, Qianlong Wen, Yanfang Ye, Chuxu Zhang:
Co-Modality Graph Contrastive Learning for Imbalanced Node Classification. - Jie Hu, Vishwaraj Doshi, Do Young Eun:
Efficiency Ordering of Stochastic Gradient Descent. - Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié:
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. - Youngin Cho, Daejin Kim, Mohammad Azam Khan, Jaegul Choo:
Mining Multi-Label Samples from Single Positive Labels. - Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò:
Sequential Information Design: Learning to Persuade in the Dark. - Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf:
AutoML Two-Sample Test. - Xinming Tu, Zhi-Jie Cao, Chenrui Xia, Sara Mostafavi, Ge Gao:
Cross-Linked Unified Embedding for cross-modality representation learning. - Haixu Ma, Donglin Zeng, Yufeng Liu:
Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion. - Dhruv Rohatgi, Vasilis Syrgkanis:
Robust Generalized Method of Moments: A Finite Sample Viewpoint. - Ziv Goldfeld, Kristjan H. Greenewald, Theshani Nuradha, Galen Reeves:
$k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension. - Nathan Kallus:
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. - Joshua Mitton, Simon Peter Mekhail, Miles J. Padgett, Daniele Faccio, Marco Aversa, Roderick Murray-Smith:
Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres. - Hunter Lang, Aravindan Vijayaraghavan, David A. Sontag:
Training Subset Selection for Weak Supervision. - Jinlong Li, Zequn Jie, Xu Wang, Xiaolin Wei, Lin Ma:
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation. - Rylan Schaeffer, Mikail Khona, Ila Fiete:
No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit. - Itai Gat, Yossi Adi, Alexander G. Schwing, Tamir Hazan:
On the Importance of Gradient Norm in PAC-Bayesian Bounds. - Marc Rigter, Bruno Lacerda, Nick Hawes:
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning. - Hyunwook Kang, Taehwan Kwon, Jinkyoo Park, James R. Morrison:
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning. - Sahil Singla, Soheil Feizi:
Improved techniques for deterministic l2 robustness. - Derek Hansen, Brian Manzo, Jeffrey Regier:
Normalizing Flows for Knockoff-free Controlled Feature Selection. - Yihong Chen, Pushkar Mishra, Luca Franceschi, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel:
ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective. - Junhong Shen, Mikhail Khodak, Ameet Talwalkar:
Efficient Architecture Search for Diverse Tasks. - Sen Lin, Li Yang, Deliang Fan, Junshan Zhang:
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer. - Xiangyu Peng, Mark O. Riedl, Prithviraj Ammanabrolu:
Inherently Explainable Reinforcement Learning in Natural Language. - Wiebke Günther, Urmi Ninad, Jonas Wahl, Jakob Runge:
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery. - Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman:
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting. - Kunal Pratap Singh, Luca Weihs, Alvaro Herrasti, Jonghyun Choi, Aniruddha Kembhavi, Roozbeh Mottaghi:
Ask4Help: Learning to Leverage an Expert for Embodied Tasks. - Arun Kumar A. V., Santu Rana, Alistair Shilton, Svetha Venkatesh:
Human-AI Collaborative Bayesian Optimisation. - Yizhen Wang, Mohannad Alhanahnah, Xiaozhu Meng, Ke Wang, Mihai Christodorescu, Somesh Jha:
Robust Learning against Relational Adversaries. - Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen:
Active Bayesian Causal Inference. - Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang:
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. - Xinyu Pi, Wanjun Zhong, Yan Gao, Nan Duan, Jian-Guang Lou:
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training. - Kelvin Cheng, Tianfu Wu, Christopher G. Healey:
Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching. - Vincent Szolnoky, Viktor Andersson, Balázs Kulcsár, Rebecka Jörnsten:
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity. - Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, Hongsheng Li:
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields. - Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré:
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. - Tackgeun You, Saehoon Kim, Chiheon Kim, Doyup Lee, Bohyung Han:
Locally Hierarchical Auto-Regressive Modeling for Image Generation. - Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David P. Wipf:
Learning Enhanced Representation for Tabular Data via Neighborhood Propagation. - Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard:
On global convergence of ResNets: From finite to infinite width using linear parameterization. - Hoyong Kim, Kangil Kim:
Spherization Layer: Representation Using Only Angles. - Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. - Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, Xiaojuan Qi:
Self-Supervised Visual Representation Learning with Semantic Grouping. - Ioannis Anagnostides, Gabriele Farina, Ioannis Panageas, Tuomas Sandholm:
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games. - Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster:
Discovered Policy Optimisation. - Anand Jerry George, Clément L. Canonne:
Robust Testing in High-Dimensional Sparse Models. - Junsheng Zhou, Baorui Ma, Yu-Shen Liu, Yi Fang, Zhizhong Han:
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds. - Tianyang Hu, Jun Wang, Wenjia Wang, Zhenguo Li:
Understanding Square Loss in Training Overparametrized Neural Network Classifiers. - Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang:
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem. - Shang Liu, Jiashuo Jiang, Xiaocheng Li:
Non-stationary Bandits with Knapsacks. - Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne:
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination. - Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell:
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. - Yu Huang, Yingbin Liang, Longbo Huang:
Provable Generalization of Overparameterized Meta-learning Trained with SGD. - Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner:
When Do Flat Minima Optimizers Work? - Ziyu Wang, Yuhao Zhou, Jun Zhu:
Fast Instrument Learning with Faster Rates. - Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen:
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? - Sang-Hoon Lee, Seung-Bin Kim, Ji-Hyun Lee, Eunwoo Song, Min-Jae Hwang, Seong-Whan Lee:
HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis. - Tomoharu Iwata, Atsutoshi Kumagai:
Sharing Knowledge for Meta-learning with Feature Descriptions. - Fuchao Wei, Chenglong Bao, Yang Liu, Guangwen Yang:
A Variant of Anderson Mixing with Minimal Memory Size. - Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, Ping Luo:
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition. - Bo Zhao, Nima Dehmamy, Robin Walters, Rose Yu:
Symmetry Teleportation for Accelerated Optimization. - Aras Selvi, Mohammad Reza Belbasi, Martin Haugh, Wolfram Wiesemann:
Wasserstein Logistic Regression with Mixed Features. - Yulong Liu, Guibo Zhu, Bin Zhu, Qi Song, Guojing Ge, Haoran Chen, Guanhui Qiao, Ru Peng, Lingxiang Wu, Jinqiao Wang:
TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training. - Zheyi Fan, Zhaohui Li, Qingpei Hu:
Robust Bayesian Regression via Hard Thresholding. - Lénaïc Chizat, Stephen Zhang, Matthieu Heitz, Geoffrey Schiebinger:
Trajectory Inference via Mean-field Langevin in Path Space. - Liting Lin, Heng Fan, Zhipeng Zhang, Yong Xu, Haibin Ling:
SwinTrack: A Simple and Strong Baseline for Transformer Tracking. - JoonHo Jang, Byeonghu Na, DongHyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon:
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation. - Zongyi Li, Miguel Liu-Schiaffini, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Learning Chaotic Dynamics in Dissipative Systems. - Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola:
Poisson Flow Generative Models. - Mathieu Molina, Patrick Loiseau:
Bounding and Approximating Intersectional Fairness through Marginal Fairness. - Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang:
Semi-infinitely Constrained Markov Decision Processes. - Robert Gieselmann, Florian T. Pokorny:
Latent Planning via Expansive Tree Search. - Byeongkeun Ahn, Chiyoon Kim, Youngjoon Hong, Hyunwoo J. Kim:
Invertible Monotone Operators for Normalizing Flows. - Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu:
Dynamic Sparse Network for Time Series Classification: Learning What to "See". - Ruijie Wang, Zheng Li, Dachun Sun, Shengzhong Liu, Jinning Li, Bing Yin, Tarek F. Abdelzaher:
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs. - Nikolaj Thams, Michael Oberst, David A. Sontag:
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. - Ming Ding, Wendi Zheng, Wenyi Hong, Jie Tang:
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers. - Zichu Liu, Lacra Pavel:
Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method. - Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn:
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning. - Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint:
Reinforcement Learning with Neural Radiance Fields. - Simon Buchholz, Michel Besserve, Bernhard Schölkopf:
Function Classes for Identifiable Nonlinear Independent Component Analysis. - Yaming Yang, Ziyu Guan, Zhe Wang, Wei Zhao, Cai Xu, Weigang Lu, Jianbin Huang:
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering. - Moitreya Chatterjee, Narendra Ahuja, Anoop Cherian:
Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation. - Yang Hu, Adam Wierman, Guannan Qu:
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory. - Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro:
coVariance Neural Networks. - Haibo Yang, Peiwen Qiu, Jia Liu:
Taming Fat-Tailed ("Heavier-Tailed" with Potentially Infinite Variance) Noise in Federated Learning. - Wei Zhai, Yang Cao, Jing Zhang, Zheng-Jun Zha:
Exploring Figure-Ground Assignment Mechanism in Perceptual Organization. - Yutong Chen, Ronglai Zuo, Fangyun Wei, Yu Wu, Shujie Liu, Brian Mak:
Two-Stream Network for Sign Language Recognition and Translation. - Zhuofan Ying, Peter Hase, Mohit Bansal:
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. - Renate Krause, Matthew Cook, Sepp Kollmorgen, Valerio Mante, Giacomo Indiveri:
Operative dimensions in unconstrained connectivity of recurrent neural networks. - Jacob Hilton, Karl Cobbe, John Schulman:
Batch size-invariance for policy optimization. - Yuting Ng, Ali Hasan, Vahid Tarokh:
Inference and Sampling for Archimax Copulas. - Jiapeng Tang, Lev Markhasin, Bi Wang, Justus Thies, Matthias Nießner:
Neural Shape Deformation Priors. - Damien Scieur, Gauthier Gidel, Quentin Bertrand, Fabian Pedregosa:
The Curse of Unrolling: Rate of Differentiating Through Optimization. - Jian Xie, Jingwei Xu, Guochang Wang, Yuan Yao, Zenan Li, Chun Cao, Hanghang Tong:
A Deep Learning Dataloader with Shared Data Preparation. - Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge:
Learning Physics Constrained Dynamics Using Autoencoders. - Mengmeng Jing, Xiantong Zhen, Jingjing Li, Cees Snoek:
Variational Model Perturbation for Source-Free Domain Adaptation. - Emmanuel Abbe, Enric Boix-Adserà:
On the non-universality of deep learning: quantifying the cost of symmetry. - Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. - Shentao Yang, Shujian Zhang, Yihao Feng, Mingyuan Zhou:
A Unified Framework for Alternating Offline Model Training and Policy Learning. - Paterne Gahungu, Christopher W. Lanyon, Mauricio A. Álvarez, Engineer Bainomugisha, Michael T. Smith, Richard Wilkinson:
Adjoint-aided inference of Gaussian process driven differential equations. - Bo Liu, Mao Ye, Stephen Wright, Peter Stone, Qiang Liu:
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. - Pierre-Cyril Aubin-Frankowski, Anna Korba, Flavien Léger:
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM. - Shi Feng, Fang-Yi Yu, Yiling Chen:
Peer Prediction for Learning Agents. - Yujia Xie, Luowei Zhou, Xiyang Dai, Lu Yuan, Nguyen Bach, Ce Liu, Michael Zeng:
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning. - Yuxiang Yang, Junjie Yang, Yufei Xu, Jing Zhang, Long Lan, Dacheng Tao:
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking. - Long Sun, Jinshan Pan, Jinhui Tang:
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution. - Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Roberto Bondesan, Herke van Hoof, Christopher Lott, Weiliang Will Zeng, Piero Zappi:
Neural Topological Ordering for Computation Graphs. - Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf:
Probable Domain Generalization via Quantile Risk Minimization. - Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov:
Locating and Editing Factual Associations in GPT. - Kennard Yanting Chan, Guosheng Lin, Haiyu Zhao, Weisi Lin:
S-PIFu: Integrating Parametric Human Models with PIFu for Single-view Clothed Human Reconstruction. - Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak:
A composable machine-learning approach for steady-state simulations on high-resolution grids. - Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, Xianglong Liu:
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models. - Han Shen, Tianyi Chen:
A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences. - Yifan Lin, Yuxuan Ren, Enlu Zhou:
Bayesian Risk Markov Decision Processes. - Shao-Heng Ko, Erin Taylor, Pankaj K. Agarwal, Kamesh Munagala:
All Politics is Local: Redistricting via Local Fairness. - Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Tran, Yi Tay, Donald Metzler:
Confident Adaptive Language Modeling. - Wenxin Li, Moran Feldman, Ehsan Kazemi, Amin Karbasi:
Submodular Maximization in Clean Linear Time. - Jasper Tan, Blake Mason, Hamid Javadi, Richard G. Baraniuk:
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. - Laurent Condat, Kai Yi, Peter Richtárik:
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. - Vishvak Murahari, Carlos E. Jimenez, Runzhe Yang, Karthik Narasimhan:
DataMUX: Data Multiplexing for Neural Networks. - Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, Uygar Sümbül:
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators. - Yongyi Su, Xun Xu, Kui Jia:
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering. - Chengyu Dong, Liyuan Liu, Jingbo Shang:
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting. - Vivien Cabannes, Francis R. Bach, Vianney Perchet, Alessandro Rudi:
Active Labeling: Streaming Stochastic Gradients. - Eldar David Abraham, Karel D'Oosterlinck, Amir Feder, Yair Ori Gat, Atticus Geiger, Christopher Potts, Roi Reichart, Zhengxuan Wu:
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior. - Pengfei Li, Beiwen Tian, Yongliang Shi, Xiaoxue Chen, Hao Zhao, Guyue Zhou, Ya-Qin Zhang:
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation. - Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Y. Zou:
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. - Kumar Krishna Agrawal, Arnab Kumar Mondal, Arna Ghosh, Blake A. Richards:
$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay. - Pierre Perrault:
When Combinatorial Thompson Sampling meets Approximation Regret. - Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu:
Pruning has a disparate impact on model accuracy. - Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Sequence Model Imitation Learning with Unobserved Contexts. - Jiajin Li, Sirui Lin, Jose H. Blanchet, Viet Anh Nguyen:
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints. - Cameron Voloshin, Hoang Minh Le, Swarat Chaudhuri, Yisong Yue:
Policy Optimization with Linear Temporal Logic Constraints. - Michael Matena, Colin Raffel:
Merging Models with Fisher-Weighted Averaging. - Hoyong Jeong, Suyoung Lee, Sung Ju Hwang, Sooel Son:
Learning to Generate Inversion-Resistant Model Explanations. - Xinhang Liu, Jiaben Chen, Huai Yu, Yu-Wing Tai, Chi-Keung Tang:
Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation. - Pierre Colombo, Eduardo Dadalto Câmara Gomes, Guillaume Staerman, Nathan Noiry, Pablo Piantanida:
Beyond Mahalanobis Distance for Textual OOD Detection. - Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin:
FasterRisk: Fast and Accurate Interpretable Risk Scores. - Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Lin:
Graph Learning Assisted Multi-Objective Integer Programming. - Khai Nguyen, Nhat Ho:
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution. - Xiaoyun Li, Ping Li:
SignRFF: Sign Random Fourier Features. - Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
The Role of Baselines in Policy Gradient Optimization. - Seunghyuk Cho, Juyong Lee, Jaesik Park, Dongwoo Kim:
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning. - Chenglin Fan, Ping Li, Xiaoyun Li:
Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate. - Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli:
Multi-Fidelity Best-Arm Identification. - Enmao Diao, Jie Ding, Vahid Tarokh:
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. - Yue Song, Nicu Sebe, Wei Wang:
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection. - Haoyi Zhou, Siyang Xiao, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li:
Jump Self-attention: Capturing High-order Statistics in Transformers. - Guojun Xiong, Shufan Wang, Jian Li:
Learning Infinite-Horizon Average-Reward Restless Multi-Action Bandits via Index Awareness. - Trung Le, Eli Shlizerman:
STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers. - Srishti Gautam, Ahcène Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer:
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. - Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse:
If Influence Functions are the Answer, Then What is the Question? - Peter Belcak, Ard Kastrati, Flavio Schenker, Roger Wattenhofer:
FACT: Learning Governing Abstractions Behind Integer Sequences. - Yangrui Chen, Cong Xie, Meng Ma, Juncheng Gu, Yanghua Peng, Haibin Lin, Chuan Wu, Yibo Zhu:
SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training. - Abhinav Kumar, Chenhao Tan, Amit Sharma:
Probing Classifiers are Unreliable for Concept Removal and Detection. - Xiyue Wang, Jinxi Xiang, Jun Zhang, Sen Yang, Zhongyi Yang, Ming-Hui Wang, Jing Zhang, Wei Yang, Junzhou Huang, Xiao Han:
SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification. - Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. - Jack Valmadre:
Hierarchical classification at multiple operating points. - Alexander Moreno, Zhenke Wu, Supriya Nagesh, Walter H. Dempsey, James M. Rehg:
Kernel Multimodal Continuous Attention. - Albert Pumarola, Artsiom Sanakoyeu, Lior Yariv, Ali K. Thabet, Yaron Lipman:
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids. - Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng:
Exact Shape Correspondence via 2D graph convolution. - Ryan R. Strauss, Junier B. Oliva:
Posterior Matching for Arbitrary Conditioning. - Xizewen Han, Huangjie Zheng, Mingyuan Zhou:
CARD: Classification and Regression Diffusion Models. - Nikolaos Tsilivis, Julia Kempe:
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness? - Tian Qin, Fengxiang He, Dingfeng Shi, Wenbing Huang, Dacheng Tao:
Benefits of Permutation-Equivariance in Auction Mechanisms. - Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg:
MoCoDA: Model-based Counterfactual Data Augmentation. - Roxana Daneshjou, Mert Yüksekgönül, Zhuo Ran Cai, Roberto A. Novoa, James Y. Zou:
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis. - Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas:
A permutation-free kernel two-sample test. - Gautam Singh, Yi-Fu Wu, Sungjin Ahn:
Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos. - Vasileios Charisopoulos, Anil Damle:
Communication-efficient distributed eigenspace estimation with arbitrary node failures. - Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. - Shashank Singh, Justin T. Khim:
Optimal Binary Classification Beyond Accuracy. - Minsoo Kang, Hyewon Yoo, Eunhee Kang, Sehwan Ki, Hyong-Euk Lee, Bohyung Han:
Information-Theoretic GAN Compression with Variational Energy-based Model. - Alexander Edmonds, Aleksandar Nikolov, Toniann Pitassi:
On Learning and Refutation in Noninteractive Local Differential Privacy. - Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum:
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters. - Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. - Qinghua Liu, Csaba Szepesvári, Chi Jin:
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. - Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu:
Advancing Model Pruning via Bi-level Optimization. - Yinqi Li, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen:
Optimal Positive Generation via Latent Transformation for Contrastive Learning. - Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar:
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge. - Solenne Gaucher, Alexandra Carpentier, Christophe Giraud:
The price of unfairness in linear bandits with biased feedback. - Yuko Ishiwaka, Xiao S. Zeng, Shun Ogawa, Donovan Westwater, Tadayuki Tone, Masaki Nakada:
DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning. - Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi:
Truncated Matrix Power Iteration for Differentiable DAG Learning. - Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, Suha Kwak:
Learning Debiased Classifier with Biased Committee. - Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence:
Modeling the Machine Learning Multiverse. - Ming-Kun Xie, Jiahao Xiao, Sheng-Jun Huang:
Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels. - Yanwei Li, Yilun Chen, Xiaojuan Qi, Zeming Li, Jian Sun, Jiaya Jia:
Unifying Voxel-based Representation with Transformer for 3D Object Detection. - Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough:
UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. - Rachit Bansal, Danish Pruthi, Yonatan Belinkov:
Measures of Information Reflect Memorization Patterns. - Jiechao Guan, Yong Liu, Zhiwu Lu:
Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms. - Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Stavroulakis:
On Scrambling Phenomena for Randomly Initialized Recurrent Networks. - Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen I. Aardal:
Learning to Branch with Tree MDPs. - Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lió, Michael M. Bronstein:
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. - Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Jiliang Tang, Weiqi Luo:
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models. - Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine:
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. - Mantas Mazeika, Eric Tang, Andy Zou, Steven Basart, Jun Shern Chan, Dawn Song, David A. Forsyth, Jacob Steinhardt, Dan Hendrycks:
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. - Andrea Tirinzoni, Rémy Degenne:
On Elimination Strategies for Bandit Fixed-Confidence Identification. - Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang:
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture. - Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, Dacheng Tao:
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits. - Jiashun Jin:
A sharp NMF result with applications in network modeling. - Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang, Congchong Nie, Jun Liu, Jinxiang Lai, Guannan Jiang, Xi Wang, Chengjie Wang:
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation. - Alex Bie, Gautam Kamath, Vikrant Singhal:
Private Estimation with Public Data. - Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu:
Pre-activation Distributions Expose Backdoor Neurons. - Sujin Jang, Joohan Na, Dokwan Oh:
DaDA: Distortion-aware Domain Adaptation for Unsupervised Semantic Segmentation. - Zeyue Xue, Jianming Liang, Guanglu Song, Zhuofan Zong, Liang Chen, Yu Liu, Ping Luo:
Large-batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes. - Rebekka Burkholz:
Most Activation Functions Can Win the Lottery Without Excessive Depth. - Yiren Zhao, Xitong Gao, Ilia Shumailov, Nicolò Fusi, Robert D. Mullins:
Rapid Model Architecture Adaption for Meta-Learning. - Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Exploring through Random Curiosity with General Value Functions. - Dae Ha Kim, Byung Cheol Song:
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition. - Manley Roberts, Pranav Mani, Saurabh Garg, Zachary C. Lipton:
Unsupervised Learning under Latent Label Shift. - Yuan Luo:
SHINE: SubHypergraph Inductive Neural nEtwork. - Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton:
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. - Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How:
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. - Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu:
Quantized Training of Gradient Boosting Decision Trees. - Shashank Srikant, Ben Lipkin, Anna A. Ivanova, Evelina Fedorenko, Una-May O'Reilly:
Convergent Representations of Computer Programs in Human and Artificial Neural Networks. - Dat Viet Thanh Nguyen, Phong Tran The, Tan M. Dinh, Cuong Pham, Anh Tran:
QC-StyleGAN - Quality Controllable Image Generation and Manipulation. - Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Zhangdaihong Liu, Xu Sun, Yang Yang, David A. Clifton:
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions. - Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya K. Singh, Pierre H. Richemond, James L. McClelland, Felix Hill:
Data Distributional Properties Drive Emergent In-Context Learning in Transformers. - Vinith M. Suriyakumar, Ashia C. Wilson:
Algorithms that Approximate Data Removal: New Results and Limitations. - Xiaojun Xu, Linyi Li, Bo Li:
LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness. - Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite:
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. - Lihao Wang, Yi Zhou, Yiqun Wang, Xiaoqing Zheng, Xuanjing Huang, Hao Zhou:
Regularized Molecular Conformation Fields. - Frederic Koehler, Elchanan Mossel:
Reconstruction on Trees and Low-Degree Polynomials. - Xinmeng Huang, Yiming Chen, Wotao Yin, Kun Yuan:
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression. - Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk:
Memory safe computations with XLA compiler. - Yibo Yang, Hong Wang, Haobo Yuan, Zhouchen Lin:
Towards Theoretically Inspired Neural Initialization Optimization. - Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, Chen Change Loy:
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies. - Guy Tennenholtz, Shie Mannor:
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning. - Yuri Kinoshita, Taiji Suzuki:
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization. - Fan Liu, Hao Liu, Wenzhao Jiang:
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models. - Bastian Jung, Fredrik D. Johansson:
Efficient learning of nonlinear prediction models with time-series privileged information. - Geng Yuan, Yanyu Li, Sheng Li, Zhenglun Kong, Sergey Tulyakov, Xulong Tang, Yanzhi Wang, Jian Ren:
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training. - Ahmed M. Alaa, Anthony Philippakis, David A. Sontag:
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography. - Juha Harviainen, Mikko Koivisto, Petteri Kaski:
Trustworthy Monte Carlo. - Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani, Benjamin I. P. Rubinstein:
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity. - Geovani Rizk, Igor Colin, Albert Thomas, Rida Laraki, Yann Chevaleyre:
An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits. - Shiqiang Wang, Mingyue Ji:
A Unified Analysis of Federated Learning with Arbitrary Client Participation. - Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski:
Deconfounded Representation Similarity for Comparison of Neural Networks. - Junyi Chai, Taeuk Jang, Xiaoqian Wang:
Fairness without Demographics through Knowledge Distillation. - Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein:
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. - Su Jia, Andrew A. Li, R. Ravi:
Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model. - Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou:
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models. - Yunzi Ding, Jonathan Niles-Weed:
Asymptotics of smoothed Wasserstein distances in the small noise regime. - Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang:
Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees. - Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang:
Pareto Set Learning for Expensive Multi-Objective Optimization. - Raunak Kumar, Robert Kleinberg:
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem. - Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens:
Efficient identification of informative features in simulation-based inference. - Christina Baek, Yiding Jiang, Aditi Raghunathan, J. Zico Kolter:
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift. - Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev:
Large-Scale Differentiable Causal Discovery of Factor Graphs. - Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M. Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour:
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. - Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attack Against Deep Neural Networks. - Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang:
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach. - Shao-Qun Zhang, Zhi-Hua Zhou:
Theoretically Provable Spiking Neural Networks. - Aleksandros Sobczyk, Mathieu Luisier:
Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss. - Fei He, Haoyang Zhang, Naiyu Gao, Jian Jia, Yanhu Shan, Xin Zhao, Kaiqi Huang:
InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation. - Deng Cai, Elman Mansimov, Yi-An Lai, Yixuan Su, Lei Shu, Yi Zhang:
Measuring and Reducing Model Update Regression in Structured Prediction for NLP. - Bohang Zhang, Du Jiang, Di He, Liwei Wang:
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective. - Yijing Liu, Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang, Zihan Zhou, Wei Chen:
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks. - Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man Cheung:
Few-shot Image Generation via Adaptation-Aware Kernel Modulation. - Mathieu Tuli, Andrew C. Li, Pashootan Vaezipoor, Toryn Q. Klassen, Scott Sanner, Sheila A. McIlraith:
Learning to Follow Instructions in Text-Based Games. - Guangxi Li, Ruilin Ye, Xuanqiang Zhao, Xin Wang:
Concentration of Data Encoding in Parameterized Quantum Circuits. - Jianfei Zhang, Jun Bai, Chenghua Lin, Yanmeng Wang, Wenge Rong:
Improving Variational Autoencoders with Density Gap-based Regularization. - Xiaoqing Tan, Zhengling Qi, Christopher W. Seymour, Lu Tang:
RISE: Robust Individualized Decision Learning with Sensitive Variables. - Ganchao Wei, Ian H. Stevenson, Xiaojing Wang:
Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers. - Yuandong Tian:
Understanding Deep Contrastive Learning via Coordinate-wise Optimization. - Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari Morcos:
Beyond neural scaling laws: beating power law scaling via data pruning. - Abir De, Soumen Chakrabarti:
Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection. - Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler, Elise van der Pol, Rita Cucchiara, Pascal Mettes:
Maximum Class Separation as Inductive Bias in One Matrix. - Yuxin Sun, Dong Lao, Ganesh Sundaramoorthi, Anthony J. Yezzi:
Surprising Instabilities in Training Deep Networks and a Theoretical Analysis. - Youngin Cho, Daejin Kim, Dongmin Kim, Mohammad Azam Khan, Jaegul Choo:
WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting. - Tao Sun, Dongsheng Li, Bao Wang:
Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks. - Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman:
Benign Underfitting of Stochastic Gradient Descent. - Clément Chadebec, Stéphanie Allassonnière:
A Geometric Perspective on Variational Autoencoders. - Randall Balestriero, Ishan Misra, Yann LeCun:
A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training. - Xiwen Liang, Yangxin Wu, Jianhua Han, Hang Xu, Chunjing Xu, Xiaodan Liang:
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving. - Diego Doimo, Aldo Glielmo, Sebastian Goldt, Alessandro Laio:
Redundant representations help generalization in wide neural networks. - Yihan Zhang, Nir Weinberger:
Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models. - Milad Sefidgaran, Romain Chor, Abdellatif Zaidi:
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning. - Yuanyu Wan, Wei-Wei Tu, Lijun Zhang:
Online Frank-Wolfe with Arbitrary Delays. - Yibo Miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao:
Isometric 3D Adversarial Examples in the Physical World. - Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal, Stephen H. Bach:
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes. - David Buterez, Jon Paul Janet, Steven J. Kiddle, Dino Oglic, Pietro Liò:
Graph Neural Networks with Adaptive Readouts. - Jung Yeon Park, Lawson L. S. Wong:
Robust Imitation of a Few Demonstrations with a Backwards Model. - Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang:
Communication Efficient Distributed Learning for Kernelized Contextual Bandits. - Antonio Terpin, Nicolas Lanzetti, Batuhan Yardim, Florian Dörfler, Giorgia Ramponi:
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions. - Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces. - Shichang Zhang, Yozen Liu, Neil Shah, Yizhou Sun:
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games. - Kang-Jun Liu, Masanori Suganuma, Takayuki Okatani:
Bridging the Gap from Asymmetry Tricks to Decorrelation Principles in Non-contrastive Self-supervised Learning. - Yasong Feng, Zengfeng Huang, Tianyu Wang:
Lipschitz Bandits with Batched Feedback. - Guan Gui, Zhen Zhao, Lei Qi, Luping Zhou, Lei Wang, Yinghuan Shi:
Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class. - Heng Dong, Tonghan Wang, Jiayuan Liu, Chongjie Zhang:
Low-Rank Modular Reinforcement Learning via Muscle Synergy. - Ming Jin, Yuan-Fang Li, Shirui Pan:
Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs. - Lisha Chen, Songtao Lu, Tianyi Chen:
Understanding Benign Overfitting in Gradient-Based Meta Learning. - Alexander W. Bergman, Petr Kellnhofer, Wang Yifan, Eric R. Chan, David B. Lindell, Gordon Wetzstein:
Generative Neural Articulated Radiance Fields. - Yifan Zhang, Haiyan Jiang, Haojie Ren, Changliang Zou, Dejing Dou:
AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control. - Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang, Yan Zheng, Yi Li, Jianye Hao, Yang Liu:
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis. - Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola:
Faster Deep Reinforcement Learning with Slower Online Network. - Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. - Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. - Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi:
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization. - Lang Huang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Toshihiko Yamasaki:
Green Hierarchical Vision Transformer for Masked Image Modeling. - Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Simultaneous Missing Value Imputation and Structure Learning with Groups. - Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe:
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. - Fan-Yun Sun, Isaac Kauvar, Ruohan Zhang, Jiachen Li, Mykel J. Kochenderfer, Jiajun Wu, Nick Haber:
Interaction Modeling with Multiplex Attention. - Steffen Schotthöfer, Emanuele Zangrando, Jonas Kusch, Gianluca Ceruti, Francesco Tudisco:
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations. - Shaohui Peng, Xing Hu, Rui Zhang, Ke Tang, Jiaming Guo, Qi Yi, Ruizhi Chen, Xishan Zhang, Zidong Du, Ling Li, Qi Guo, Yunji Chen:
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning. - Louis Béthune, Thibaut Boissin, Mathieu Serrurier, Franck Mamalet, Corentin Friedrich, Alberto González-Sanz:
Pay attention to your loss : understanding misconceptions about Lipschitz neural networks. - Peter Conway Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap:
Large-Scale Retrieval for Reinforcement Learning. - Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion:
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. - Nils M. Kriege:
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited. - Junyuan Hong, Lingjuan Lyu, Jiayu Zhou, Michael Spranger:
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling. - Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, Yang Yu:
Multi-agent Dynamic Algorithm Configuration. - Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni:
TaSIL: Taylor Series Imitation Learning. - Mojmir Mutny, Andreas Krause:
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces. - Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup:
Continuous MDP Homomorphisms and Homomorphic Policy Gradient. - Dohyun Kwon, Ying Fan, Kangwook Lee:
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance. - Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart:
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game. - Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. - Lucas Baudin, Rida Laraki:
Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions. - Yangze Zhou, Gitta Kutyniok, Bruno Ribeiro:
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs. - Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Algorithms with Prediction Portfolios. - Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng:
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs. - Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher:
Instability and Local Minima in GAN Training with Kernel Discriminators. - Shuwen Yang, Zhihao Yang, Dong Li, Yingxue Zhang, Zhanguang Zhang, Guojie Song, Jianye Hao:
Versatile Multi-stage Graph Neural Network for Circuit Representation. - Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai:
Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret. - Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Optimal and Adaptive Monteiro-Svaiter Acceleration. - Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson:
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. - Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer G. Dy:
SparCL: Sparse Continual Learning on the Edge. - Blair L. Bilodeau, Linbo Wang, Daniel M. Roy:
Adaptively Exploiting d-Separators with Causal Bandits. - Yangru Huang, Peixi Peng, Yifan Zhao, Guangyao Chen, Yonghong Tian:
Spectrum Random Masking for Generalization in Image-based Reinforcement Learning. - Chunyu Wei, Jian Liang, Di Liu, Fei Wang:
Contrastive Graph Structure Learning via Information Bottleneck for Recommendation. - Lei Chai, Ming Li:
Pyramid Attention For Source Code Summarization. - Xuefeng Du, Gabriel Gozum, Yifei Ming, Yixuan Li:
SIREN: Shaping Representations for Detecting Out-of-Distribution Objects. - Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet T. Tran, Fei Tang, Hubert Ramsauer, David P. Kreil, Michael Kopp, Günter Klambauer, Angela Bitto, Sepp Hochreiter:
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. - Jiechuan Jiang, Zongqing Lu:
I2Q: A Fully Decentralized Q-Learning Algorithm. - Dilip Arumugam, Satinder Singh:
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. - Jianxin Li, Shuai Zhang, Hui Xiong, Haoyi Zhou:
AutoST: Towards the Universal Modeling of Spatio-temporal Sequences. - Yuping Zheng, Andrew G. Lamperski:
Constrained Langevin Algorithms with L-mixing External Random Variables. - Shizhe Chen, Pierre-Louis Guhur, Makarand Tapaswi, Cordelia Schmid, Ivan Laptev:
Language Conditioned Spatial Relation Reasoning for 3D Object Grounding. - Ren Wang, Jiayue Wang, Tae Sung Kim, Jinsung Kim, Hyuk-Jae Lee:
MVP-N: A Dataset and Benchmark for Real-World Multi-View Object Classification. - Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham:
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. - Anshuman Chhabra, Ashwin Sekhari, Prasant Mohapatra:
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses. - Fan Wang, Adams Wai-Kin Kong:
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection. - Shahaf E. Finder, Yair Zohav, Maor Ashkenazi, Eran Treister:
Wavelet Feature Maps Compression for Image-to-Image CNNs. - Veronika Koren, Stefano Panzeri:
Biologically plausible solutions for spiking networks with efficient coding. - Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. - Andrew Joseph Dudzik, Petar Velickovic:
Graph Neural Networks are Dynamic Programmers. - Fredrik Hellström, Giuseppe Durisi:
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness. - Yuhang Jiang, Jianzhun Shao, Shuncheng He, Hongchang Zhang, Xiangyang Ji:
SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning. - Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang:
Self-Aware Personalized Federated Learning. - Syed Ashar Javed, Dinkar Juyal, Harshith Padigela, Amaro Taylor-Weiner, Limin Yu, Aaditya Prakash:
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology. - Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zachary Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, Jamie Shotton:
Model-Based Imitation Learning for Urban Driving. - Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Online Training Through Time for Spiking Neural Networks. - Antoine Guédon, Pascal Monasse, Vincent Lepetit:
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration. - Shunyu Yao, Howard Chen, John Yang, Karthik Narasimhan:
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents. - Yao Zhu, Yuefeng Chen, Chuanlong Xie, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Bolun Zheng, Yaowu Chen:
Boosting Out-of-distribution Detection with Typical Features. - Anand Kalvit, Assaf Zeevi:
Dynamic Learning in Large Matching Markets. - Yibo Jiang, Victor Veitch:
Invariant and Transportable Representations for Anti-Causal Domain Shifts. - Yusuke Tanaka, Tomoharu Iwata, Naonori Ueda:
Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data. - Alkis Kalavasis, Grigoris Velegkas, Amin Karbasi:
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes. - Zhenbin Wang, Mao Ye, Xiatian Zhu, Liuhan Peng, Liang Tian, Yingying Zhu:
MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification. - Alexander Genkin, David Lipshutz, Siavash Golkar, Tiberiu Tesileanu, Dmitri B. Chklovskii:
Biological Learning of Irreducible Representations of Commuting Transformations. - Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat, Kumar Avinava Dubey, Manzil Zaheer:
A Fourier Approach to Mixture Learning. - Jay Yoon Lee, Dhruvesh Patel, Purujit Goyal, Wenlong Zhao, Zhiyang Xu, Andrew McCallum:
Structured Energy Network As a Loss. - Naitong Chen, Zuheng Xu, Trevor Campbell:
Bayesian inference via sparse Hamiltonian flows. - Hao Lu, Wenze Liu, Zixuan Ye, Hongtao Fu, Yuliang Liu, Zhiguo Cao:
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling. - Yonggan Fu, Yang Zhang, Kaizhi Qian, Zhifan Ye, Zhongzhi Yu, Cheng-I Jeff Lai, Celine Lin:
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing. - Gal Vardi, Gilad Yehudai, Ohad Shamir:
Gradient Methods Provably Converge to Non-Robust Networks. - Victor Boutin, Lakshya Singhal, Xavier Thomas, Thomas Serre:
Diversity vs. Recognizability: Human-like generalization in one-shot generative models. - Xinyu Zhou, Raef Bassily:
Task-level Differentially Private Meta Learning. - Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. - Gene Li, Cong Ma, Nati Srebro:
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets. - Shinsaku Sakaue, Taihei Oki:
Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions. - Md Mofijul Islam, Reza Mirzaiee, Alexi Gladstone, Haley N. Green, Tariq Iqbal:
CAESAR: An Embodied Simulator for Generating Multimodal Referring Expression Datasets. - Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. - Chendi Qian, Gaurav Rattan, Floris Geerts, Mathias Niepert, Christopher Morris:
Ordered Subgraph Aggregation Networks. - Qiang Fu, Lun Du, Haitao Mao, Xu Chen, Wei Fang, Shi Han, Dongmei Zhang:
Neuron with Steady Response Leads to Better Generalization. - Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz, Nicki Skafte Detlefsen, Søren Hauberg:
Laplacian Autoencoders for Learning Stochastic Representations. - Jing Xu, Xu Luo, Xinglin Pan, Yanan Li, Wenjie Pei, Zenglin Xu:
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid. - Zhaomin Wu, Qinbin Li, Bingsheng He:
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. - Wonwoong Cho, Ziyu Gong, David I. Inouye:
Cooperative Distribution Alignment via JSD Upper Bound. - Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe:
An Analytical Theory of Curriculum Learning in Teacher-Student Networks. - Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning. - Neeraj Wagh, Jionghao Wei, Samarth Rawal, Brent M. Berry, Yogatheesan Varatharajah:
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts. - Wei-Ning Hsu, Bowen Shi:
u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality. - Zaixi Zhang, Qi Liu, Qingyong Hu, Chee-Kong Lee:
Hierarchical Graph Transformer with Adaptive Node Sampling. - Yiding Jiang, Evan Zheran Liu, Benjamin Eysenbach, J. Zico Kolter, Chelsea Finn:
Learning Options via Compression. - Hadi Mohasel Afshar, Sally Cripps:
Fixed-Distance Hamiltonian Monte Carlo. - Emanuele Marconato, Andrea Passerini, Stefano Teso:
GlanceNets: Interpretable, Leak-proof Concept-based Models. - Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi:
3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds. - Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski:
Masked Prediction: A Parameter Identifiability View. - Armin W. Thomas, Christopher Ré, Russell A. Poldrack:
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data. - Mingyang Yi, Ruoyu Wang, Zhi-Ming Ma:
Characterization of Excess Risk for Locally Strongly Convex Population Risk. - Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro:
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. - Jihan Yang, Shaoshuai Shi, Runyu Ding, Zhe Wang, Xiaojuan Qi:
Towards Efficient 3D Object Detection with Knowledge Distillation. - Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven Chu-Hong Hoi:
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning. - Wei Zhang, Yanjun Han, Zhengyuan Zhou, Aaron Flores, Tsachy Weissman:
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions. - Wenhao Gao, Tianfan Fu, Jimeng Sun, Connor W. Coley:
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization. - Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao:
MGNNI: Multiscale Graph Neural Networks with Implicit Layers. - Philipp Oberdiek, Gernot A. Fink, Matthias Rottmann:
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs. - Xian Liu, Qianyi Wu, Hang Zhou, Yuanqi Du, Wayne Wu, Dahua Lin, Ziwei Liu:
Audio-Driven Co-Speech Gesture Video Generation. - Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frédéric Precioso, Stefano Melacci, Adrian Weller, Pietro Lió, Mateja Jamnik:
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off. - Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, Chao Wu:
DENSE: Data-Free One-Shot Federated Learning. - Yichao Cao, Xiu Su, Qingfei Tang, Shan You, Xiaobo Lu, Chang Xu:
Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition. - Haojie Zhang, Ge Li, Jia Li, Zhongjin Zhang, Yuqi Zhu, Zhi Jin:
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively. - Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt:
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. - CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong:
Stars: Tera-Scale Graph Building for Clustering and Learning. - Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann:
Score-Based Diffusion meets Annealed Importance Sampling. - Lingfeng Sun, Haichao Zhang, Wei Xu, Masayoshi Tomizuka:
PaCo: Parameter-Compositional Multi-task Reinforcement Learning. - Zhenhong Sun, Ce Ge, Junyan Wang, Ming C. Lin, Hesen Chen, Hao Li, Xiuyu Sun:
Entropy-Driven Mixed-Precision Quantization for Deep Network Design. - Eugene Golikov, Greg Yang:
Non-Gaussian Tensor Programs. - Xingsi Dong, Zilong Ji, Tianhao Chu, Tiejun Huang, Wenhao Zhang, Si Wu:
Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks. - Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, Nigel Collier:
A Contrastive Framework for Neural Text Generation. - Felix Leeb, Stefan Bauer, Michel Besserve, Bernhard Schölkopf:
Exploring the Latent Space of Autoencoders with Interventional Assays. - Clément Chadebec, Louis J. Vincent, Stéphanie Allassonnière:
Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case. - Zhisheng Xiao, Tian Han:
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model. - Chao Qin, Zheng Wen, Xiuyuan Lu, Benjamin Van Roy:
An Analysis of Ensemble Sampling. - Alexander Soen, Ibrahim M. Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie:
Fair Wrapping for Black-box Predictions. - Alireza Fallah, Ali Makhdoumi, Azarakhsh Malekian, Asuman E. Ozdaglar:
Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms. - Rui Wang, Robin Walters, Rose Yu:
Meta-Learning Dynamics Forecasting Using Task Inference. - Diana Cai, Ryan P. Adams:
Multi-fidelity Monte Carlo: a pseudo-marginal approach. - Xuan Zhang, Necdet Serhat Aybat, Mert Gürbüzbalaban:
SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems. - Michael Zhang, Christopher Ré:
Contrastive Adapters for Foundation Model Group Robustness. - Zonglin Li, Ruiqi Guo, Sanjiv Kumar:
Decoupled Context Processing for Context Augmented Language Modeling. - Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon:
LISA: Learning Interpretable Skill Abstractions from Language. - Dmitry Kovalev, Alexander V. Gasnikov, Peter Richtárik:
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling. - Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy:
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data. - Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang:
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. - Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang:
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement. - Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik:
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. - Alexis Thual, Quang Huy Tran, Tatiana Zemskova, Nicolas Courty, Rémi Flamary, Stanislas Dehaene, Bertrand Thirion:
Aligning individual brains with fused unbalanced Gromov Wasserstein. - Lukasz Augustyniak, Kamil Tagowski, Albert Sawczyn, Denis Janiak, Roman Bartusiak, Adrian Szymczak, Arkadiusz Janz, Piotr Szymanski, Marcin Watroba, Mikolaj Morzy, Tomasz Kajdanowicz, Maciej Piasecki:
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish. - Jun Fang, Mingze Xu, Hao Chen, Bing Shuai, Zhuowen Tu, Joseph Tighe:
An In-depth Study of Stochastic Backpropagation. - Yi Tay, Vinh Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Prakash Gupta, Tal Schuster, William W. Cohen, Donald Metzler:
Transformer Memory as a Differentiable Search Index. - Yuhuai Wu, Felix Li, Percy Liang:
Insights into Pre-training via Simpler Synthetic Tasks. - Eduard Gorbunov, Adrien B. Taylor, Gauthier Gidel:
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities. - Biao Zhang, Matthias Nießner, Peter Wonka:
3DILG: Irregular Latent Grids for 3D Generative Modeling. - Runyu Zhang, Qinghua Liu, Huan Wang, Caiming Xiong, Na Li, Yu Bai:
Policy Optimization for Markov Games: Unified Framework and Faster Convergence. - Shane Bergsma, Timothy Zeyl, Javad Rahimipour Anaraki, Lei Guo:
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting. - Peilin Zhou, Zeqiang Wang, Dading Chong, Zhijiang Guo, Yining Hua, Zichang Su, Zhiyang Teng, Jiageng Wu, Jie Yang:
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets. - Yulei Niu, Long Chen, Chang Zhou, Hanwang Zhang:
Respecting Transfer Gap in Knowledge Distillation. - Arash A. Amini, Richard Baumgartner, Dai Feng:
Target alignment in truncated kernel ridge regression. - Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish:
Continual Learning In Environments With Polynomial Mixing Times. - Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler:
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts. - Keyu Yan, Man Zhou, Jie Huang, Feng Zhao, Chengjun Xie, Chongyi Li, Danfeng Hong:
Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network. - Bill Yuchen Lin, Kangmin Tan, Chris Miller, Beiwen Tian, Xiang Ren:
Unsupervised Cross-Task Generalization via Retrieval Augmentation. - Paul Masset, Jacob A. Zavatone-Veth, J. Patrick Connor, Venkatesh Murthy, Cengiz Pehlevan:
Natural gradient enables fast sampling in spiking neural networks. - Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, Dacheng Tao:
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach. - Chaobing Song, Cheuk Yin Lin, Stephen J. Wright, Jelena Diakonikolas:
Coordinate Linear Variance Reduction for Generalized Linear Programming. - Yuxuan Zhao, Alex Townsend, Madeleine Udell:
Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data. - Chien Lu, Jaakko Peltonen:
Gaussian Copula Embeddings. - Yuan Shen, Wei-Chiu Ma, Shenlong Wang:
SGAM: Building a Virtual 3D World through Simultaneous Generation and Mapping. - Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Heather Zheng, Ben Y. Zhao:
Finding Naturally Occurring Physical Backdoors in Image Datasets. - Zijian Zhang, Zhou Zhao, Zhijie Lin:
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models. - Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. - Andrea Bragagnolo, Enzo Tartaglione, Marco Grangetto:
To update or not to update? Neurons at equilibrium in deep models. - Udaya Ghai, Zhou Lu, Elad Hazan:
Non-convex online learning via algorithmic equivalence. - Chang Chen, Min Li, Zhihua Wu, Dianhai Yu, Chao Yang:
TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training. - Michael Matena, Colin Raffel:
A Combinatorial Perspective on the Optimization of Shallow ReLU Networks. - Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa:
Large Language Models are Zero-Shot Reasoners. - Mycal Tucker, Roger Levy, Julie A. Shah, Noga Zaslavsky:
Trading off Utility, Informativeness, and Complexity in Emergent Communication. - Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan D. Wegner, Konrad Schindler:
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. - Tao Zhong, Zhixiang Chi, Li Gu, Yang Wang, Yuanhao Yu, Jin Tang:
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts. - Georgios Piliouras, Ryann Sim, Stratis Skoulakis:
Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update. - Runze Liu, Fengshuo Bai, Yali Du, Yaodong Yang:
Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning. - Shuwen Chai, Jinghui Chen:
One-shot Neural Backdoor Erasing via Adversarial Weight Masking. - Ibrahim M. Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai:
Revisiting Neural Scaling Laws in Language and Vision. - Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu:
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent. - Vijay Prakash Dwivedi, Ladislav Rampásek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini:
Long Range Graph Benchmark. - Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber:
On the inability of Gaussian process regression to optimally learn compositional functions. - Ofer Yehuda, Avihu Dekel, Guy Hacohen, Daphna Weinshall:
Active Learning Through a Covering Lens. - Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton:
Uplifting Bandits. - Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou:
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning. - El Mehdi Achour, Armand Foucault, Sébastien Gerchinovitz, François Malgouyres:
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks. - Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, Shuicheng Yan:
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine. - Chen Henry Wu, Saman Motamed, Shaunak Srivastava, Fernando De la Torre:
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models. - Arun Mallya, Ting-Chun Wang, Ming-Yu Liu:
Implicit Warping for Animation with Image Sets. - Bohan Zeng, Boyu Liu, Hong Li, Xuhui Liu, Jianzhuang Liu, Dapeng Chen, Wei Peng, Baochang Zhang:
FNeVR: Neural Volume Rendering for Face Animation. - Sean Yang, Bernease Herman, Bill Howe:
Ontologue: Declarative Benchmark Construction for Ontological Multi-Label Classification. - Yu Hsuan Li, Tzu-Yin Chao, Ching-Chun Huang, Pin-Yu Chen, Wei-Chen Chiu:
Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis. - Jiaying Lin, Yuen Hei Yeung, Rynson W. H. Lau:
Exploiting Semantic Relations for Glass Surface Detection. - Raman Arora, Raef Bassily, Cristóbal Guzmán, Michael Menart, Enayat Ullah:
Differentially Private Generalized Linear Models Revisited. - Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan T. Chakaravarthy, Yogish Sabharwal, Sayan Ranu:
GREED: A Neural Framework for Learning Graph Distance Functions. - Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Open Set Label Shift. - Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Furong Huang:
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning. - Yewen Li, Chaojie Wang, Zhibin Duan, Dongsheng Wang, Bo Chen, Bo An, Mingyuan Zhou:
Alleviating "Posterior Collapse" in Deep Topic Models via Policy Gradient. - Xinyun Zhu, Yu Qi, Gang Pan, Yueming Wang:
Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding. - William de Vazelhes, Hualin Zhang, Huimin Wu, Xiaotong Yuan, Bin Gu:
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity. - Aritra Mitra, Arman Adibi, George J. Pappas, Hamed Hassani:
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds. - Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. - Chengxu Zhuang, Ziyu Xiang, Yoon Bai, Xiaoxuan Jia, Nicholas B. Turk-Browne, Kenneth A. Norman, James J. DiCarlo, Dan Yamins:
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? - Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung:
Stochastic Multiple Target Sampling Gradient Descent. - Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan:
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment. - Jacob Granley, Lucas Relic, Michael Beyeler:
Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses. - Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal:
Tractable Function-Space Variational Inference in Bayesian Neural Networks. - Yizhao Gao, Nanyi Fei, Haoyu Lu, Zhiwu Lu, Hao Jiang, Yijie Li, Zhao Cao:
BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling. - Yimeng Min, Frederik Wenkel, Michael Perlmutter, Guy Wolf:
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem? - Hung Tran-The, Sunil Gupta, Santu Rana, Tuan Truong, Long Tran-Thanh, Svetha Venkatesh:
Expected Improvement for Contextual Bandits. - Ying Nie, Kai Han, Haikang Diao, Chuanjian Liu, Enhua Wu, Yunhe Wang:
Redistribution of Weights and Activations for AdderNet Quantization. - Yunqi Miao, Alexandros Lattas, Jiankang Deng, Jungong Han, Stefanos Zafeiriou:
Physically-Based Face Rendering for NIR-VIS Face Recognition. - Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, Lei Chen, Michalis Vazirgiannis:
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection. - Jack Umenberger, Max Simchowitz, Juan C. Perdomo, Kaiqing Zhang, Russ Tedrake:
Globally Convergent Policy Search for Output Estimation. - Harsh Rangwani, Sumukh K. Aithal, Mayank Mishra, Venkatesh Babu R.:
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data. - Anton Plaksin, Stepan Martyanov:
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis. - Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan:
Partial Identification of Treatment Effects with Implicit Generative Models. - Allen Liu, Jerry Li, Ankur Moitra:
Robust Model Selection and Nearly-Proper Learning for GMMs. - Zhuoer Xu, Guanghui Zhu, Changhua Meng, Shiwen Cui, Zhenzhe Ying, Weiqiang Wang, Ming Gu, Yihua Huang:
A2: Efficient Automated Attacker for Boosting Adversarial Training. - Jon Hasselgren, Nikolai Hofmann, Jacob Munkberg:
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising. - Holden Lee, Jianfeng Lu, Yixin Tan:
Convergence for score-based generative modeling with polynomial complexity. - Mohit Yadav, Daniel R. Sheldon, Cameron Musco:
Kernel Interpolation with Sparse Grids. - Xuran Pan, Tianzhu Ye, Dongchen Han, Shiji Song, Gao Huang:
Contrastive Language-Image Pre-Training with Knowledge Graphs. - Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani:
Reconstructing Training Data From Trained Neural Networks. - Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi:
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth. - Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, Song Han:
On-Device Training Under 256KB Memory. - Nur Muhammad Shafiullah, Zichen Jeff Cui, Ariuntuya Altanzaya, Lerrel Pinto:
Behavior Transformers: Cloning $k$ modes with one stone. - Moritz Hardt, Meena Jagadeesan, Celestine Mendler-Dünner:
Performative Power. - Ankit Gupta, Albert Gu, Jonathan Berant:
Diagonal State Spaces are as Effective as Structured State Spaces. - Man Zhou, Hu Yu, Jie Huang, Feng Zhao, Jinwei Gu, Chen Change Loy, Deyu Meng, Chongyi Li:
Deep Fourier Up-Sampling. - Yan Li, Xinjiang Lu, Yaqing Wang, Dejing Dou:
Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement. - Dongsung Huh, Avinash Baidya:
The Missing Invariance Principle found - the Reciprocal Twin of Invariant Risk Minimization. - Jorio Cocola:
Signal Recovery with Non-Expansive Generative Network Priors. - Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li:
Towards Understanding the Mixture-of-Experts Layer in Deep Learning. - Maura Pintor, Luca Demetrio, Angelo Sotgiu, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli:
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples. - Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie:
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. - Adrien Vacher, François-Xavier Vialard:
Parameter tuning and model selection in Optimal Transport with semi-dual Brenier formulation. - Miran Heo, Sukjun Hwang, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim:
VITA: Video Instance Segmentation via Object Token Association. - Nathan Tsoi, Kate Candon, Deyuan Li, Yofti Milkessa, Marynel Vázquez:
Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers. - Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke:
Truncated proposals for scalable and hassle-free simulation-based inference. - Tianying Ji, Yu Luo, Fuchun Sun, Mingxuan Jing, Fengxiang He, Wenbing Huang:
When to Update Your Model: Constrained Model-based Reinforcement Learning. - Yangdi Lu, Yang Bo, Wenbo He:
Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling. - Minting Pan, Xiangming Zhu, Yunbo Wang, Xiaokang Yang:
Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models. - Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, Bernard Ghanem:
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies. - Andrew M. Childs, Tongyang Li, Jin-Peng Liu, Chunhao Wang, Ruizhe Zhang:
Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants. - Masanobu Horie, Naoto Mitsume:
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions. - Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov:
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL. - Rodrigo Veiga, Ludovic Stephan, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová:
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks. - Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi:
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. - Viraj Prabhu, Sriram Yenamandra, Aaditya Singh, Judy Hoffman:
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency. - Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani, Ali Gholami, Linh Tran, Ali Mahdavi-Amiri, Ghassan Hamarneh:
MaskTune: Mitigating Spurious Correlations by Forcing to Explore. - Eirik Lund Flogard, Ole Jakob Mengshoel:
A Dataset for Efforts Towards Achieving the Sustainable Development Goal of Safe Working Environments. - Sosuke Kobayashi, Eiichi Matsumoto, Vincent Sitzmann:
Decomposing NeRF for Editing via Feature Field Distillation. - Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Sample-Then-Optimize Batch Neural Thompson Sampling. - Danyang Tu, Wei Sun, Xiongkuo Min, Guangtao Zhai, Wei Shen:
Video-based Human-Object Interaction Detection from Tubelet Tokens. - Sayan Bhattacharya, Silvio Lattanzi, Nikos Parotsidis:
Efficient and Stable Fully Dynamic Facility Location. - Vikram Voleti, Alexia Jolicoeur-Martineau, Chris Pal:
MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation. - Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez:
Addressing Leakage in Concept Bottleneck Models. - Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Adaptive Oracle-Efficient Online Learning. - Chuanxia Zheng, Tung-Long Vuong, Jianfei Cai, Dinh Phung:
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation. - Xiang Huang, Zhanhong Ye, Hongsheng Liu, Shi Ji, Zidong Wang, Kang Yang, Yang Li, Min Wang, Haotian Chu, Fan Yu, Bei Hua, Lei Chen, Bin Dong:
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations. - Jiawei Du, Daquan Zhou, Jiashi Feng, Vincent Y. F. Tan, Joey Tianyi Zhou:
Sharpness-Aware Training for Free. - Lan V. Truong:
Generalization Error Bounds on Deep Learning with Markov Datasets. - Florent Bonnet, Jocelyn Ahmed Mazari, Paola Cinnella, Patrick Gallinari:
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions. - Vignesh Subramanian, Rahul Arya, Anant Sahai:
Generalization for multiclass classification with overparameterized linear models. - Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan:
Inception Transformer. - Jinzhi Zhang, Ruofan Tang, Zheng Cao, Jing Xiao, Ruqi Huang, Lu Fang:
ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis. - Ali Kavis, Stratis Skoulakis, Kimon Antonakopoulos, Leello Tadesse Dadi, Volkan Cevher:
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization. - Idan Amir, Roi Livni, Nati Srebro:
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. - Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Generalization Properties of NAS under Activation and Skip Connection Search. - Shuqi Wang, Valentin Schmutz, Guillaume Bellec, Wulfram Gerstner:
Mesoscopic modeling of hidden spiking neurons. - Sanghyeok Lee, Minkyu Jeon, Injae Kim, Yunyang Xiong, Hyunwoo J. Kim:
SageMix: Saliency-Guided Mixup for Point Clouds. - Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song:
Denoising Diffusion Restoration Models. - Anish Chakrabarty, Swagatam Das:
On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks. - Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. - Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Tractable Optimality in Episodic Latent MABs. - Idan Attias, Steve Hanneke, Yishay Mansour:
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability. - Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. - Fei Ye, Adrian G. Bors:
Task-Free Continual Learning via Online Discrepancy Distance Learning. - Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu, Jiawei Chen, Xu Tan, Danilo P. Mandic, Lei He, Xiangyang Li, Tao Qin, Sheng Zhao, Tie-Yan Liu:
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis. - Chirag Raman, José Vargas Quiros, Stephanie Tan, Ashraful Islam, Ekin Gedik, Hayley Hung:
ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild. - Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob L. Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karén Simonyan:
Flamingo: a Visual Language Model for Few-Shot Learning. - Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro:
Understanding the Eluder Dimension. - Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth:
Predictive Querying for Autoregressive Neural Sequence Models. - Himangi Mittal, Pedro Morgado, Unnat Jain, Abhinav Gupta:
Learning State-Aware Visual Representations from Audible Interactions. - Jinzhi Bu, David Simchi-Levi, Chonghuan Wang:
Context-Based Dynamic Pricing with Partially Linear Demand Model. - Jun Zeng, Mingyang Kou, Hailong Yao:
NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis. - Hezhen Hu, Weilun Wang, Wengang Zhou, Houqiang Li:
Hand-Object Interaction Image Generation. - Chih-Hui Ho, Nuno Vasconcelos:
DISCO: Adversarial Defense with Local Implicit Functions. - Julia Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey E. Markowitz, Sandeep R. Datta, Alex H. Williams, Scott W. Linderman:
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs. - Rui Yang, Chenjia Bai, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, Lei Han:
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing. - Haoran Sun, Hanjun Dai, Dale Schuurmans:
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces. - Vindula Jayawardana, Catherine Tang, Sirui Li, Dajiang Suo, Cathy Wu:
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning. - Siyu Long, Yi Zhou, Xinyu Dai, Hao Zhou:
Zero-Shot 3D Drug Design by Sketching and Generating. - Xiang Chen, Lei Li, Ningyu Zhang, Xiaozhuan Liang, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen:
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning. - Diego Cifuentes, Ankur Moitra:
Polynomial time guarantees for the Burer-Monteiro method. - Zhiyu Zhang, Ashok Cutkosky, Yannis Paschalidis:
Optimal Comparator Adaptive Online Learning with Switching Cost. - Ilija Bogunovic, Zihan Li, Andreas Krause, Jonathan Scarlett:
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits. - Diptarka Chakraborty, Syamantak Das, Arindam Khan, Aditya Subramanian:
Fair Rank Aggregation. - Omar Besbes, Will Ma, Omar Mouchtaki:
Beyond IID: data-driven decision-making in heterogeneous environments. - Rahul Patel, Justin Dumouchelle, Elias B. Khalil, Merve Bodur:
Neur2SP: Neural Two-Stage Stochastic Programming. - Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. - Yao Lai, Yao Mu, Ping Luo:
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning. - Shiwei Zeng, Jie Shen:
List-Decodable Sparse Mean Estimation. - Xiaoling Hu:
Structure-Aware Image Segmentation with Homotopy Warping. - Jonathan Wilton, Abigail M. Y. Koay, Ryan K. L. Ko, Miao Xu, Nan Ye:
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization. - Adrian Valente, Jonathan W. Pillow, Srdjan Ostojic:
Extracting computational mechanisms from neural data using low-rank RNNs. - Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Gaussian Mixture Models. - Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami:
A Fast Post-Training Pruning Framework for Transformers. - Kuan Yew Leong, Siew Mooi Lim:
SurDis: A Surface Discontinuity Dataset for Wearable Technology to Assist Blind Navigation in Urban Environments. - Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer:
Interventions, Where and How? Experimental Design for Causal Models at Scale. - Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z. Sheng, Leman Akoglu, Charu C. Aggarwal:
Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection. - Yuan Deng, Vahab Mirrokni, Hanrui Zhang:
Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers. - Cansu Sancaktar, Sebastian Blaes, Georg Martius:
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation. - Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu:
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning. - Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu:
Active Learning Polynomial Threshold Functions. - Will Greedy, Heng Wei Zhu, Joseph Pemberton, Jack Mellor, Rui Ponte Costa:
Single-phase deep learning in cortico-cortical networks. - Yu Ding, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, Fang Chen:
Domain Generalization by Learning and Removing Domain-specific Features. - Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi S. Jaakkola:
Torsional Diffusion for Molecular Conformer Generation. - Mojan Javaheripi, Gustavo de Rosa, Subhabrata Mukherjee, Shital Shah, Tomasz Religa, Caio César Teodoro Mendes, Sébastien Bubeck, Farinaz Koushanfar, Debadeepta Dey:
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models. - Wenkai Xu, Gesine D. Reinert:
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators. - Yue Gao, Ilia Shumailov, Kassem Fawaz, Nicolas Papernot:
On the Limitations of Stochastic Pre-processing Defenses. - Siddhesh Khandelwal, Leonid Sigal:
Iterative Scene Graph Generation. - Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher:
Proximal Point Imitation Learning. - Haoxuan Qu, Li Xu, Yujun Cai, Lin Geng Foo, Jun Liu:
Heatmap Distribution Matching for Human Pose Estimation. - Zekang Zhang, Guangyu Gao, Zhiyuan Fang, Jianbo Jiao, Yunchao Wei:
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation. - Martin Jørgensen, Michael A. Osborne:
Bezier Gaussian Processes for Tall and Wide Data. - Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek, Aleksandr Petiushko, Ivan V. Oseledets:
Smoothed Embeddings for Certified Few-Shot Learning. - Mark Kozdoba, Edward Moroshko, Shie Mannor, Yacov Crammer:
Finite Sample Analysis Of Dynamic Regression Parameter Learning. - Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil:
Group Meritocratic Fairness in Linear Contextual Bandits. - Gautam Kamath, Argyris Mouzakis, Vikrant Singhal:
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. - Ashish Kumar Jayant, Shalabh Bhatnagar:
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm. - Ziyin Liu, Botao Li, Xiangming Meng:
Exact Solutions of a Deep Linear Network. - Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee, Tze-Yun Leong:
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects. - Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, Rama Chellappa:
FeLMi : Few shot Learning with hard Mixup. - Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang, Peter Wonka:
EpiGRAF: Rethinking training of 3D GANs. - Arsène Fansi Tchango, Rishab Goel, Julien Martel, Zhi Wen, Gaétan Marceau-Caron, Joumana Ghosn:
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning. - Xiuying Chen, Mingzhe Li, Xin Gao, Xiangliang Zhang:
Towards Improving Faithfulness in Abstractive Summarization. - Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui:
ZIN: When and How to Learn Invariance Without Environment Partition? - Yong Liu, Siqi Mai, Minhao Cheng, Xiangning Chen, Cho-Jui Hsieh, Yang You:
Random Sharpness-Aware Minimization. - Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth:
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. - Lingjiao Chen, Zhihua Jin, Sabri Eyuboglu, Christopher Ré, Matei Zaharia, James Y. Zou:
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions. - Zihan Zhang, Yuhang Jiang, Yuan Zhou, Xiangyang Ji:
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning. - Liang Chen, Yong Zhang, Yibing Song, Jue Wang, Lingqiao Liu:
OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training. - Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre M. Bayen, Yi Wu:
The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games. - Daniel Widdowson, Vitaliy Kurlin:
Resolving the data ambiguity for periodic crystals. - Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune:
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos. - Xuhai Xu, Han Zhang, Yasaman S. Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin S. Kuehn, Mike A. Merrill, Paula S. Nurius, Shwetak N. Patel, Tim Althoff, Margaret E. Morris, Eve A. Riskin, Jennifer Mankoff, Anind K. Dey:
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. - Carlos Soto, Karthik Bharath, Matthew Reimherr, Aleksandra B. Slavkovic:
Shape And Structure Preserving Differential Privacy. - Sungjun Cho, Seonwoo Min, Jinwoo Kim, Moontae Lee, Honglak Lee, Seunghoon Hong:
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost. - Maribeth Rauh, John Mellor, Jonathan Uesato, Po-Sen Huang, Johannes Welbl, Laura Weidinger, Sumanth Dathathri, Amelia Glaese, Geoffrey Irving, Iason Gabriel, William Isaac, Lisa Anne Hendricks:
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models. - Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Hongbo Deng, Jian Xu, Bo Zheng:
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction. - Rongjun Qin, Xingyuan Zhang, Songyi Gao, Xiong-Hui Chen, Zewen Li, Weinan Zhang, Yang Yu:
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning. - Xufeng Cai, Chaobing Song, Cristóbal Guzmán, Jelena Diakonikolas:
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions. - Jianhao Ding, Tong Bu, Zhaofei Yu, Tiejun Huang, Jian K. Liu:
SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training. - Zixin Wen, Yuanzhi Li:
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning. - Kimia Noorbakhsh, Manuel Gomez-Rodriguez:
Counterfactual Temporal Point Processes. - Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, Denny Zhou:
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. - Xudong Pan, Shengyao Zhang, Mi Zhang, Yifan Yan, Min Yang:
House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography. - Brian Hu Zhang, Tuomas Sandholm:
Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games. - Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Küttler, Naila Murray:
Dungeons and Data: A Large-Scale NetHack Dataset. - Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in LQR Control. - Charles Guille-Escuret, Adam Ibrahim, Baptiste Goujaud, Ioannis Mitliagkas:
Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound. - Gene Chou, Ilya Chugunov, Felix Heide:
GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions. - Mancheng Meng, Ziyan Wu, Terrence Chen, Xiran Cai, Xiang Sean Zhou, Fan Yang, Dinggang Shen:
Forecasting Human Trajectory from Scene History. - Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, Jian Tang:
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure. - Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert:
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality. - Andrew Davison:
Asymptotics of ℓ2 Regularized Network Embeddings. - Kaiwei Che, Luziwei Leng, Kaixuan Zhang, Jianguo Zhang, Qinghu Meng, Jie Cheng, Qinghai Guo, Jianxing Liao:
Differentiable hierarchical and surrogate gradient search for spiking neural networks. - Yury Gorishniy, Ivan Rubachev, Artem Babenko:
On Embeddings for Numerical Features in Tabular Deep Learning. - Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, Alexei A. Efros:
Visual Prompting via Image Inpainting. - Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, Andreas Geiger:
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction. - Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang:
OpenAUC: Towards AUC-Oriented Open-Set Recognition. - Cuneyt Gurcan Akcora, Murat Kantarcioglu, Yulia R. Gel, Baris Coskunuzer:
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT. - Hanbo Chen, Jiawei Yang, Daniel Maxim Iascone, Lijuan Liu, Lei He, Hanchuan Peng, Jianhua Yao:
TreeMoCo: Contrastive Neuron Morphology Representation Learning. - Zhenlin Xu, Marc Niethammer, Colin Raffel:
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language. - Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, Jonathan Tompson:
Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions. - Miguel Ángel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Joshua M. Susskind:
GAUDI: A Neural Architect for Immersive 3D Scene Generation. - Tao Yu, Zhizheng Zhang, Cuiling Lan, Yan Lu, Zhibo Chen:
Mask-based Latent Reconstruction for Reinforcement Learning. - Renzhe Xu, Xingxuan Zhang, Bo Li, Yafeng Zhang, Xiaolong Chen, Peng Cui:
Product Ranking for Revenue Maximization with Multiple Purchases. - Yiming Zhu, Hongyu Liu, Yibing Song, Ziyang Yuan, Xintong Han, Chun Yuan, Qifeng Chen, Jue Wang:
One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations. - Wouter M. Koolen, Muriel Felipe Pérez-Ortiz:
Luckiness in Multiscale Online Learning. - Haonan Wang, Wei Huang, Ziwei Wu, Hanghang Tong, Andrew Margenot, Jingrui He:
Deep Active Learning by Leveraging Training Dynamics. - Alkis Kalavasis, Konstantinos Stavropoulos, Emmanouil Zampetakis:
Learning and Covering Sums of Independent Random Variables with Unbounded Support. - Haoyu Lu, Mingyu Ding, Nanyi Fei, Yuqi Huo, Zhiwu Lu:
LGDN: Language-Guided Denoising Network for Video-Language Modeling. - Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik, Arnold W. M. Smeulders:
LieGG: Studying Learned Lie Group Generators. - Diptodip Deb, Zhenfei Jiao, Ruth R. Sims, Alex B. Chen, Michael Broxton, Misha B. Ahrens, Kaspar Podgorski, Srinivas C. Turaga:
FourierNets enable the design of highly non-local optical encoders for computational imaging. - Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. - Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon:
Discovery of Single Independent Latent Variable. - Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida:
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. - Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev:
LAION-5B: An open large-scale dataset for training next generation image-text models. - Muhammad Firmansyah Kasim, Yi Heng Lim:
Constants of motion network. - Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi:
HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction. - Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander V. Gasnikov, Peter Richtárik, Martin Takác:
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate. - Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald:
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel. - Gérard Ben Arous, Reza Gheissari, Aukosh Jagannath:
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling. - Jiaming Liu, Xiaojian Xu, Weijie Gan, Shirin Shoushtari, Ulugbek Kamilov:
Online Deep Equilibrium Learning for Regularization by Denoising. - Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, DJ Strouse, Jane Wang, Andrea Banino, Felix Hill:
Semantic Exploration from Language Abstractions and Pretrained Representations. - Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung:
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. - Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter:
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. - Leonid Iosipoi, Anton Vakhrushev:
SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems. - Yanchen Deng, Shufeng Kong, Caihua Liu, Bo An:
Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems. - Shenao Zhang:
Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning. - Binhang Yuan, Yongjun He, Jared Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Ré, Ce Zhang:
Decentralized Training of Foundation Models in Heterogeneous Environments. - Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan:
Cross Aggregation Transformer for Image Restoration. - Daphne Cornelisse, Thomas Rood, Yoram Bachrach, Mateusz Malinowski, Tal Kachman:
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members. - Lokesh Nagalapatti, Guntakanti Sai Koushik, Abir De, Sunita Sarawagi:
Learning Recourse on Instance Environment to Enhance Prediction Accuracy. - Dandan Guo, Zhuo Li, Meixi Zheng, He Zhao, Mingyuan Zhou, Hongyuan Zha:
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification. - Ruizhong Qiu, Zhiqing Sun, Yiming Yang:
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems. - Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári:
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs. - Ziyi Chen, Shaocong Ma, Yi Zhou:
Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach. - Zhaoyu Li, Xujie Si:
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems. - Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang:
Brain Network Transformer. - Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, Xing Xie, Hao Sun, Weiwei Deng, Qi Zhang, Mao Yang:
A Neural Corpus Indexer for Document Retrieval. - Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos:
Perfect Sampling from Pairwise Comparisons. - Rasmus Pagh, Mikkel Thorup:
Improved Utility Analysis of Private CountSketch. - Steven Yin, Christian Kroer:
Optimal Efficiency-Envy Trade-Off via Optimal Transport. - Yipeng Kang, Tonghan Wang, Qianlan Yang, Xiaoran Wu, Chongjie Zhang:
Non-Linear Coordination Graphs. - Chejian Xu, Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li:
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles. - Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye:
Improving Diffusion Models for Inverse Problems using Manifold Constraints. - Zhaowei Cai, Avinash Ravichandran, Paolo Favaro, Manchen Wang, Davide Modolo, Rahul Bhotika, Zhuowen Tu, Stefano Soatto:
Semi-supervised Vision Transformers at Scale. - Hideaki Kim, Taichi Asami, Hiroyuki Toda:
Fast Bayesian Estimation of Point Process Intensity as Function of Covariates. - Maxime Haddouche, Benjamin Guedj:
Online PAC-Bayes Learning. - Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, Xinchao Wang:
Deep Model Reassembly. - Francisco Criado, David Martínez-Rubio, Sebastian Pokutta:
Fast Algorithms for Packing Proportional Fairness and its Dual. - Mingcheng Hou, Issei Sato:
A Closer Look at Prototype Classifier for Few-shot Image Classification. - Qiwen Cui, Simon S. Du:
When are Offline Two-Player Zero-Sum Markov Games Solvable? - Jason A. Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda, Sunny Kang, Rosaline Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott, Matthias Samwald, Stephen H. Bach, Stella Biderman, Mario Sänger, Bo Wang, Alison Callahan, Daniel León Periñán, Théo Gigant, Patrick Haller, Jenny Chim, José D. Posada, John M. Giorgi, Karthik Rangasai Sivaraman, Marc Pàmies, Marianna Nezhurina, Robert Martin, Michael Cullan, Moritz Freidank, Nathan Dahlberg, Shubhanshu Mishra, Shamik Bose, Nicholas Broad, Yanis Labrak, Shlok Deshmukh, Sid Kiblawi, Ayush Singh, Minh Chien Vu, Trishala Neeraj, Jonas Golde, Albert Villanova del Moral, Benjamin Beilharz:
BigBio: A Framework for Data-Centric Biomedical Natural Language Processing. - Xinyi Hu, Jasper C. H. Lee, Jimmy H. M. Lee, Allen Z. Zhong:
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize. - Peng Yu, Albert Bifet, Jesse Read, Chao Xu:
Linear tree shap. - Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey:
Gradient Estimation with Discrete Stein Operators. - Hyunwoong Chang, Changwoo J. Lee, Zhao Tang Luo, Huiyan Sang, Quan Zhou:
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems. - Anurag Ajay, Abhishek Gupta, Dibya Ghosh, Sergey Levine, Pulkit Agrawal:
Distributionally Adaptive Meta Reinforcement Learning. - Gaurang Sriramanan, Maharshi Gor, Soheil Feizi:
Toward Efficient Robust Training against Union of $\ell_p$ Threat Models. - Shudong Huang, Hongjie Wu, Yazhou Ren, Ivor W. Tsang, Zenglin Xu, Wentao Feng, Jiancheng Lv:
Multi-view Subspace Clustering on Topological Manifold. - Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li:
CLEAR: Generative Counterfactual Explanations on Graphs. - Vinod Raman, Ambuj Tewari:
Online Agnostic Multiclass Boosting. - Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento:
A contrastive rule for meta-learning. - Jacob P. Portes, Christian Schmid, James M. Murray:
Distinguishing Learning Rules with Brain Machine Interfaces. - Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret:
Efficient Training of Low-Curvature Neural Networks. - Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Infinite-Fidelity Coregionalization for Physical Simulation. - Julien Cornebise, Ivan Orsolic, Freddie Kalaitzis:
Open High-Resolution Satellite Imagery: The WorldStrat Dataset - With Application to Super-Resolution. - Charles Lovering, Jessica Forde, George Konidaris, Ellie Pavlick, Michael L. Littman:
Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. - Haiming Xu, Lingqiao Liu, Qiuchen Bian, Zhen Yang:
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization. - Hanxu Zhou, Qixuan Zhou, Zhenyuan Jin, Tao Luo, Yaoyu Zhang, Zhi-Qin John Xu:
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width. - Hui En Pang, Zhongang Cai, Lei Yang, Tianwei Zhang, Ziwei Liu:
Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms. - Konstantin Sozykin, Andrei Chertkov, Roman Schutski, Anh-Huy Phan, Andrzej S. Cichocki, Ivan V. Oseledets:
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning. - William Brown, Arpit Agarwal:
Diversified Recommendations for Agents with Adaptive Preferences. - Andrew Zhao, Matthieu Gaetan Lin, Yangguang Li, Yong-Jin Liu, Gao Huang:
A Mixture Of Surprises for Unsupervised Reinforcement Learning. - Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel:
Deep Hierarchical Planning from Pixels. - Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Christopher B. Choy, Anima Anandkumar, Minsu Cho, Jaesik Park:
PeRFception: Perception using Radiance Fields. - Lingxiao Huang, Yuyi Wang, Chunxue Yang, Huanjian Zhou:
Efficient Submodular Optimization under Noise: Local Search is Robust. - Peizhong Ju, Xiaojun Lin, Ness B. Shroff:
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model. - Qing Xiu, Kai Han, Jing Tang, Shuang Cui, He Huang:
Chromatic Correlation Clustering, Revisited. - Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. - Vikram Gupta, Sumegh Roychowdhury, Mithun Das, Somnath Banerjee, Punyajoy Saha, Binny Mathew, Hastagiri Prakash Vanchinathan, Animesh Mukherjee:
Multilingual Abusive Comment Detection at Scale for Indic Languages. - Jia-Qi Yang, De-Chuan Zhan:
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems. - Mingrui Liu, Zhenxun Zhuang, Yunwen Lei, Chunyang Liao:
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. - Kamil Deja, Anna Kuzina, Tomasz Trzcinski, Jakub M. Tomczak:
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. - Xinsong Ma, Zekai Wang, Weiwei Liu:
On the Tradeoff Between Robustness and Fairness. - Qi Wang, Herke van Hoof:
Learning Expressive Meta-Representations with Mixture of Expert Neural Processes. - Jiaqi Han, Wenbing Huang, Hengbo Ma, Jiachen Li, Josh Tenenbaum, Chuang Gan:
Learning Physical Dynamics with Subequivariant Graph Neural Networks. - Ram Dyuthi Sristi, Gal Mishne, Ariel Jaffe:
DiSC: Differential Spectral Clustering of Features. - Tim Franzmeyer, Philip H. S. Torr, João F. Henriques:
Learn what matters: cross-domain imitation learning with task-relevant embeddings. - Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, Neil Houlsby:
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes. - James A. D. Gardner, Bernhard Egger, William Smith:
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior. - Stephen Zhao, Chris Lu, Roger B. Grosse, Jakob N. Foerster:
Proximal Learning With Opponent-Learning Awareness. - Guillaume Lample, Timothée Lacroix, Marie-Anne Lachaux, Aurélien Rodriguez, Amaury Hayat, Thibaut Lavril, Gabriel Ebner, Xavier Martinet:
HyperTree Proof Search for Neural Theorem Proving. - Ruoyu Cheng, Xianglong Lyu, Yang Li, Junjie Ye, Jianye Hao, Junchi Yan:
The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design. - Fabien Pesquerel, Odalric-Ambrym Maillard:
IMED-RL: Regret optimal learning of ergodic Markov decision processes. - Ruomin Huang, Jiawei Huang, Wenjie Liu, Hu Ding:
Coresets for Wasserstein Distributionally Robust Optimization Problems. - Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani:
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections. - Jérôme Bolte, Edouard Pauwels, Samuel Vaiter:
Automatic differentiation of nonsmooth iterative algorithms. - Jiaxi Gu, Xiaojun Meng, Guansong Lu, Lu Hou, Niu Minzhe, Xiaodan Liang, Lewei Yao, Runhui Huang, Wei Zhang, Xin Jiang, Chunjing Xu, Hang Xu:
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark. - Alessandro Abate, Alec Edwards, Mirco Giacobbe:
Neural Abstractions. - Zhiying Jiang, Yiqin Dai, Ji Xin, Ming Li, Jimmy Lin:
Few-Shot Non-Parametric Learning with Deep Latent Variable Model. - Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, Hongsheng Li:
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning. - Giyoung Jeon, Haedong Jeong, Jaesik Choi:
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network. - Weiran Yao, Guangyi Chen, Kun Zhang:
Temporally Disentangled Representation Learning. - Lue Tao, Lei Feng, Hongxin Wei, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen:
Can Adversarial Training Be Manipulated By Non-Robust Features? - Mandi Zhao, Pieter Abbeel, Stephen James:
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning. - Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao:
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning. - Yonatan Bitton, Nitzan Bitton Guetta, Ron Yosef, Yuval Elovici, Mohit Bansal, Gabriel Stanovsky, Roy Schwartz:
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models. - Tero Karras, Miika Aittala, Timo Aila, Samuli Laine:
Elucidating the Design Space of Diffusion-Based Generative Models. - Jiawei Guan, Feng Zhang, Jiesong Liu, Hsin-Hsuan Sung, Ruofan Wu, Xiaoyong Du, Xipeng Shen:
TREC: Transient Redundancy Elimination-based Convolution. - Soon Hoe Lim, Yijun Wan, Umut Simsekli:
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent. - Linan Yue, Qi Liu, Yichao Du, Yanqing An, Li Wang, Enhong Chen:
DARE: Disentanglement-Augmented Rationale Extraction. - Xuan Son Nguyen:
The Gyro-Structure of Some Matrix Manifolds. - Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Bo Song, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, Dengdeng Yu, Matthew Poon, Animesh Garg:
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments. - Jing Liu, Chulin Xie, Sanmi Koyejo, Bo Li:
CoPur: Certifiably Robust Collaborative Inference via Feature Purification. - Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan:
Toward Understanding Privileged Features Distillation in Learning-to-Rank. - Randall Balestriero, Yann LeCun:
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. - Brian Hu Zhang, Luca Carminati, Federico Cacciamani, Gabriele Farina, Pierriccardo Olivieri, Nicola Gatti, Tuomas Sandholm:
Subgame Solving in Adversarial Team Games. - Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau:
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. - Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis:
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. - Jan Harold Alcantara, Ching-pei Lee:
Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems. - Conglong Li, Minjia Zhang, Yuxiong He:
The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models. - Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper:
Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders. - Bohan Wang, Qi Meng, Huishuai Zhang, Ruoyu Sun, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
Does Momentum Change the Implicit Regularization on Separable Data? - Mingtian Zhang, Peter Hayes, David Barber:
Generalization Gap in Amortized Inference. - Marc Jourdan, Rémy Degenne, Dorian Baudry, Rianne de Heide, Emilie Kaufmann:
Top Two Algorithms Revisited. - Xiang Chen, Zhixian Yang, Xiaojun Wan:
Relation-Constrained Decoding for Text Generation. - Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, Edward Grefenstette:
Learning General World Models in a Handful of Reward-Free Deployments. - Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson:
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens. - Jörg K. H. Franke, Frederic Runge, Frank Hutter:
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design. - Maxwell A. Xu, Alexander Moreno, Supriya Nagesh, Varol Burak Aydemir, David W. Wetter, Santosh Kumar, James M. Rehg:
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation. - Jeff Z. HaoChen, Colin Wei, Ananya Kumar, Tengyu Ma:
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations. - Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski:
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm. - Pierre Colombo, Nathan Noiry, Ekhine Irurozki, Stéphan Clémençon:
What are the best Systems? New Perspectives on NLP Benchmarking. - Jianxin Zhang, Yutong Wang, Clayton Scott:
Learning from Label Proportions by Learning with Label Noise. - Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou:
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. - Yunrui Yu, Xitong Gao, Cheng-Zhong Xu:
MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack. - Elfarouk Harb, Kent Quanrud, Chandra Chekuri:
Faster and Scalable Algorithms for Densest Subgraph and Decomposition. - Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study. - Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori, Kato Takashi, Yuhei Umeda, Venkatesh Babu R.:
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics. - Zhong-Cheng Wu, Ting-Zhu Huang, Liang-Jian Deng, Hong-Xia Dou, Deyu Meng:
Tensor Wheel Decomposition and Its Tensor Completion Application. - Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu:
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. - Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Andreas Krause, Ilija Bogunovic:
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems. - Erika Lu, Forrester Cole, Weidi Xie, Tali Dekel, Bill Freeman, Andrew Zisserman, Michael Rubinstein:
Associating Objects and Their Effects in Video through Coordination Games. - Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, Hongsheng Li:
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training. - Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan:
Exploring Example Influence in Continual Learning. - Luca Pesce, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová:
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap. - Junyi Chai, Xiaoqian Wang:
Self-Supervised Fair Representation Learning without Demographics. - Rodrigo Hormazabal, Changyoung Park, Soonyoung Lee, Sehui Han, Yeonsik Jo, Jaewan Lee, Ahra Jo, Seung Hwan Kim, Jaegul Choo, Moontae Lee, Honglak Lee:
CEDe: A collection of expert-curated datasets with atom-level entity annotations for Optical Chemical Structure Recognition. - Qi Zhang, Yifei Wang, Yisen Wang:
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders. - Qixun Wang, Yifei Wang, Hong Zhu, Yisen Wang:
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors. - Arian R. Jamasb, Ramón Viñas Torné, Eric Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lió, Tom L. Blundell:
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks. - Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He:
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. - Sudhanshu Chanpuriya, Cameron Musco:
Simplified Graph Convolution with Heterophily. - Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi:
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. - Eric Wu, Nora Brackbill, Alexander Sher, Alan M. Litke, Eero P. Simoncelli, E. J. Chichilnisky:
Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors. - Christos Thrampoulidis, Ganesh Ramachandra Kini, Vala Vakilian, Tina Behnia:
Imbalance Trouble: Revisiting Neural-Collapse Geometry. - Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou:
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. - Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, Sung-Ju Lee:
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation. - Yixiong Zou, Shanghang Zhang, Yuhua Li, Ruixuan Li:
Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation. - Mao Ye, Lemeng Wu, Qiang Liu:
First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data. - Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks:
Forecasting Future World Events With Neural Networks. - Ruofan Wu, Junmin Zhong, Brent Wallace, Xiang Gao, He Huang, Jennie Si:
Human-Robotic Prosthesis as Collaborating Agents for Symmetrical Walking. - Hanbyul Lee, Qifan Song, Jean Honorio:
Support Recovery in Sparse PCA with Incomplete Data. - Anders Aamand, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner:
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks. - Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi:
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks. - Siqi Liang, Yan Sun, Faming Liang:
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network. - Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs:
Autoregressive Perturbations for Data Poisoning. - Qitian Wu, Wentao Zhao, Zenan Li, David P. Wipf, Junchi Yan:
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. - Alireza Ghaffari, Marzieh S. Tahaei, Mohammadreza Tayaranian, Masoud Asgharian, Vahid Partovi Nia:
Is Integer Arithmetic Enough for Deep Learning Training? - Sizhe An, Yin Li, Ümit Y. Ogras:
mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors. - Jing Yuan, Panagiotis Barmpoutis, Tania Stathaki:
Effectiveness of Vision Transformer for Fast and Accurate Single-Stage Pedestrian Detection. - Ilker Demirel, Ahmet Alparslan Celik, Cem Tekin:
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine. - Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai Wan, Mingqian Tang, Rong Jin, Yue Gao:
Grow and Merge: A Unified Framework for Continuous Categories Discovery. - Yanjie Ze, Xiaolong Wang:
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset. - Utkarsh Mall, Bharath Hariharan, Kavita Bala:
Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery. - Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He:
Improved Algorithms for Neural Active Learning. - Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. - Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. - Jin Zhang, Siyuan Li, Chongjie Zhang:
CUP: Critic-Guided Policy Reuse. - Joachim Bona-Pellissier, François Malgouyres, François Bachoc:
Local Identifiability of Deep ReLU Neural Networks: the Theory. - Yao Mu, Yuzheng Zhuang, Fei Ni, Bin Wang, Jianyu Chen, Jianye Hao, Ping Luo:
DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning. - Yibo Zeng, Henry Lam:
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization. - Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin Choi:
QUARK: Controllable Text Generation with Reinforced Unlearning. - Mingyang Liu, Chengjie Wu, Qihan Liu, Yansen Jing, Jun Yang, Pingzhong Tang, Chongjie Zhang:
Safe Opponent-Exploitation Subgame Refinement. - Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu:
Parameter-free Dynamic Graph Embedding for Link Prediction. - Wenying Deng, Beau Coker, Rajarshi Mukherjee, Jeremiah Z. Liu, Brent A. Coull:
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees. - Joseph Early, Tom Bewley, Christine Evers, Sarvapali D. Ramchurn:
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning. - Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic:
Explaining Preferences with Shapley Values. - Frank Nussbaum, Jakob Gawlikowski, Julia Niebling:
Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks. - Xianli Zeng, Edgar Dobriban, Guang Cheng:
Fair Bayes-Optimal Classifiers Under Predictive Parity. - Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson:
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. - Yurong You, Cheng Perng Phoo, Katie Luo, Travis Zhang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger:
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving. - Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, Ryan Lowe:
Training language models to follow instructions with human feedback. - Arnhav Datar, Arun Rajkumar, John Augustine:
Byzantine Spectral Ranking. - Yang Li, Tatsuya Harada:
Non-rigid Point Cloud Registration with Neural Deformation Pyramid. - Maria-Florina Balcan, Misha Khodak, Dravyansh Sharma, Ameet Talwalkar:
Provably tuning the ElasticNet across instances. - Xuan Li, Yadi Cao, Minchen Li, Yin Yang, Craig A. Schroeder, Chenfanfu Jiang:
PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration. - Alex J. Chan, Mihaela van der Schaar:
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning. - Zhan Yu, Hongshun Yao, Mujin Li, Xin Wang:
Power and limitations of single-qubit native quantum neural networks. - Debarun Bhattacharjya, Radu Marinescu:
Hedging as Reward Augmentation in Probabilistic Graphical Models. - Hongda Sun, Shufang Xie, Shuqi Li, Yuhan Chen, Ji-Rong Wen, Rui Yan:
Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records. - Olivier Jeunen, Ciarán M. Gilligan-Lee, Rishabh Mehrotra, Mounia Lalmas:
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders. - Xuehai Pan, Mickel Liu, Fangwei Zhong, Yaodong Yang, Song-Chun Zhu, Yizhou Wang:
MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control. - Yiqing Xu, Wei Gao, David Hsu:
Receding Horizon Inverse Reinforcement Learning. - Cheongjae Jang, Sungyoon Lee, Frank C. Park, Yung-Kyun Noh:
A Reparametrization-Invariant Sharpness Measure Based on Information Geometry. - Konstantin Schürholt, Boris Knyazev, Xavier Giró-i-Nieto, Damian Borth:
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights. - Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, Igor Mordatch:
Multi-Game Decision Transformers. - Tan Nguyen, Tam Nguyen, Hai Do, Khai Nguyen, Vishwanath Saragadam, Minh Pham, Duy Khuong Nguyen, Nhat Ho, Stanley J. Osher:
Improving Transformer with an Admixture of Attention Heads. - William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood:
Flexible Diffusion Modeling of Long Videos. - Zihan Liu, Yun Luo, Lirong Wu, Zicheng Liu, Stan Z. Li:
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias. - Andac Demir, Baris Coskunuzer, Yulia R. Gel, Ignacio Segovia-Dominguez, Yuzhou Chen, Bulent Kiziltan:
ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery. - Jian Wang, Miaomiao Zhang:
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers. - Miri Zilka, Bradley Butcher, Adrian Weller:
A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets. - Junyu Xie, Weidi Xie, Andrew Zisserman:
Segmenting Moving Objects via an Object-Centric Layered Representation. - Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter:
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. - Weijian Deng, Stephen Gould, Liang Zheng:
On the Strong Correlation Between Model Invariance and Generalization. - Yash Pote, Kuldeep S. Meel:
On Scalable Testing of Samplers. - Yoav Kolumbus, Noam Nisan:
How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents. - Milad Leyli-Abadi, Antoine Marot, Jérôme Picault, David Danan, Mouadh Yagoubi, Benjamin Donnot, Seif Attoui, Pavel Dimitrov, Asma Farjallah, Clement Etienam:
LIPS - Learning Industrial Physical Simulation benchmark suite. - Alix Leroy, Graham K. Taylor:
FlyView: a bio-informed optical flow truth dataset for visual navigation using panoramic stereo vision. - Tao Meng, Sidi Lu, Nanyun Peng, Kai-Wei Chang:
Controllable Text Generation with Neurally-Decomposed Oracle. - Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah D. Goodman:
Active Learning Helps Pretrained Models Learn the Intended Task. - Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani:
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments. - Dat Do, Nhat Ho, XuanLong Nguyen:
Beyond black box densities: Parameter learning for the deviated components. - Sloan Nietert, Ziv Goldfeld, Ritwik Sadhu, Kengo Kato:
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances. - Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. - Xiaopeng Yu, Jiechuan Jiang, Wanpeng Zhang, Haobin Jiang, Zongqing Lu:
Model-Based Opponent Modeling. - Tal Shaharabany, Yoad Tewel, Lior Wolf:
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs. - Siqi Xu, Lin Liu, Zhonghua Liu:
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning. - Shiqian Li, Kewen Wu, Chi Zhang, Yixin Zhu:
On the Learning Mechanisms in Physical Reasoning. - Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet:
A Continuous Time Framework for Discrete Denoising Models. - Gagan Aggarwal, Kshipra Bhawalkar, Aranyak Mehta, Divyarthi Mohan, Alexandros Psomas:
Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions. - Matthew Sutton, Robert Salomone, Augustin Chevallier, Paul Fearnhead:
Continuously Tempered PDMP samplers. - Miguel Suau, Jinke He, Mustafa Mert Çelikok, Matthijs T. J. Spaan, Frans A. Oliehoek:
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems. - Qing Guo, Junya Chen, Dong Wang, Yuewei Yang, Xinwei Deng, Jing Huang, Lawrence Carin, Fan Li, Chenyang Tao:
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization. - Peisong Wen, Qianqian Xu, Zhiyong Yang, Yuan He, Qingming Huang:
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability. - Siqi Wang, Tee Hiang Cheng, Meng-Hiot Lim:
LTMD: Learning Improvement of Spiking Neural Networks with Learnable Thresholding Neurons and Moderate Dropout. - Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv:
TransBoost: Improving the Best ImageNet Performance using Deep Transduction. - Meihua Dang, Anji Liu, Guy Van den Broeck:
Sparse Probabilistic Circuits via Pruning and Growing. - Yushun Zhang, Congliang Chen, Naichen Shi, Ruoyu Sun, Zhi-Quan Luo:
Adam Can Converge Without Any Modification On Update Rules. - James Vuckovic:
Nonlinear MCMC for Bayesian Machine Learning. - Binghui Peng, Andrej Risteski:
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions. - Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Ran Zhang, Chin-Yi Cheng, Jiajun Wu:
IKEA-Manual: Seeing Shape Assembly Step by Step. - Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao, Zhangyang Wang:
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design. - Zhijing Jin, Sydney Levine, Fernando Gonzalez Adauto, Ojasv Kamal, Maarten Sap, Mrinmaya Sachan, Rada Mihalcea, Josh Tenenbaum, Bernhard Schölkopf:
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment. - Gene Li, Junbo Li, Anmol Kabra, Nati Srebro, Zhaoran Wang, Zhuoran Yang:
Exponential Family Model-Based Reinforcement Learning via Score Matching. - Lei Song, Ke Xue, Xiaobin Huang, Chao Qian:
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization. - Cheng Jiang, Asadur Chowdury, Xinhai Hou, Akhil Kondepudi, Christian W. Freudiger, Kyle Conway, Sandra Camelo-Piragua, Daniel A. Orringer, Honglak Lee, Todd C. Hollon:
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology. - Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah D. Goodman, Dorsa Sadigh:
Assistive Teaching of Motor Control Tasks to Humans. - Lilly Kumari, Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes:
Retrospective Adversarial Replay for Continual Learning. - Shaoan Xie, Qirong Ho, Kun Zhang:
Unsupervised Image-to-Image Translation with Density Changing Regularization. - Yuefan Wu, Zeyuan Chen, Shaowei Liu, Zhongzheng Ren, Shenlong Wang:
CASA: Category-agnostic Skeletal Animal Reconstruction. - Botao Hao, Tor Lattimore:
Regret Bounds for Information-Directed Reinforcement Learning. - Matthias Englert, Ranko Lazic:
Adversarial Reprogramming Revisited. - Shichao Kan, Yixiong Liang, Min Li, Yigang Cen, Jianxin Wang, Zhihai He:
Coded Residual Transform for Generalizable Deep Metric Learning. - Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta:
When Does Differentially Private Learning Not Suffer in High Dimensions? - Shinji Ito, Taira Tsuchiya, Junya Honda:
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. - Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu, Debadeepta Dey, Wenhui Wang, Xiang Zhang, Ahmed Hassan Awadallah, Jianfeng Gao:
Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models. - Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas:
Universal Rates for Interactive Learning. - Peihao Chen, Dongyu Ji, Kunyang Lin, Weiwen Hu, Wenbing Huang, Thomas H. Li, Mingkui Tan, Chuang Gan:
Learning Active Camera for Multi-Object Navigation. - Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz:
Knowledge Distillation: Bad Models Can Be Good Role Models. - Marcelo Arenas, Pablo Barceló, Miguel A. Romero Orth, Bernardo Subercaseaux:
On Computing Probabilistic Explanations for Decision Trees. - Po-Yao Huang, Hu Xu, Juncheng Li, Alexei Baevski, Michael Auli, Wojciech Galuba, Florian Metze, Christoph Feichtenhofer:
Masked Autoencoders that Listen. - KrishnaTeja Killamsetty, Guttu Sai Abhishek, Aakriti, Ganesh Ramakrishnan, Alexandre V. Evfimievski, Lucian Popa, Rishabh K. Iyer:
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning. - Khaled Nakhleh, I-Hong Hou:
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs. - Sarthak Mittal, Yoshua Bengio, Guillaume Lajoie:
Is a Modular Architecture Enough? - Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger:
Exploration via Planning for Information about the Optimal Trajectory. - Matthew Fahrbach, Gang Fu, Mehrdad Ghadiri:
Subquadratic Kronecker Regression with Applications to Tensor Decomposition. - Marnix Suilen, Thiago D. Simão, David Parker, Nils Jansen:
Robust Anytime Learning of Markov Decision Processes. - Yuezhi Yang, Hao Pan:
Discovering Design Concepts for CAD Sketches. - Zhiyuan Yao, Zihan Ding:
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game. - Junlong Lyu, Zhitang Chen, Chang Feng, Wenjing Cun, Shengyu Zhu, Yanhui Geng, Zhijie Xu, Chen Yongwei:
Para-CFlows: $C^k$-universal diffeomorphism approximators as superior neural surrogates. - Souhaib Attaiki, Maks Ovsjanikov:
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching. - Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. - Konstantina Dritsa, Aikaterini Thoma, Ioannis Pavlopoulos, Panos Louridas:
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis. - Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William M. Wuest, Amarda Shehu, Liang Zhao:
Multi-objective Deep Data Generation with Correlated Property Control. - Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun:
Domain Adaptation meets Individual Fairness. And they get along. - Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan:
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem. - Feihu Huang, Junyi Li, Shangqian Gao, Heng Huang:
Enhanced Bilevel Optimization via Bregman Distance. - Gamaleldin F. Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C. Mozer, Thomas Kipf:
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. - Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. - Liangzu Peng, Christian Kümmerle, René Vidal:
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression. - Hsiang Hsu, Flávio P. Calmon:
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification. - Boyi Liu, Jiayang Li, Zhuoran Yang, Hoi-To Wai, Mingyi Hong, Yu (Marco) Nie, Zhaoran Wang:
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence. - Theodor Misiakiewicz, Song Mei:
Learning with convolution and pooling operations in kernel methods. - Jiaqi Wang, Roei Schuster, Ilia Shumailov, David Lie, Nicolas Papernot:
In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning. - Feiyi Xiao, Junjie Tang, Huaying Fang, Ruibin Xi:
Estimating graphical models for count data with applications to single-cell gene network. - Daniel Lee, Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications. - Hongjing Niu, Hanting Li, Feng Zhao, Bin Li:
Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge. - Haris Aziz, Alexander Lam, Mashbat Suzuki, Toby Walsh:
Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism. - Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie:
Distributionally robust weighted k-nearest neighbors. - Puyuan Liu, Xiang Zhang, Lili Mou:
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization. - Ruikun Zhou, Thanin Quartz, Hans De Sterck, Jun Liu:
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees. - Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant:
Minimax Regret for Cascading Bandits. - Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. - Paavo Parmas, Takuma Seno:
Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms. - Xiaosu Zhu, Jingkuan Song, Yu Lei, Lianli Gao, Hengtao Shen:
A Lower Bound of Hash Codes' Performance. - Zhilu Zhang, Rongjian Xu, Ming Liu, Zifei Yan, Wangmeng Zuo:
Self-Supervised Image Restoration with Blurry and Noisy Pairs. - Yang Jin, Yongzhi Li, Zehuan Yuan, Yadong Mu:
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding. - Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. - Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, Daniel E. Ho:
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset. - Laurent Valentin Jospin, Allen Antony, Lian Xu, Hamid Laga, Farid Boussaïd, Mohammed Bennamoun:
Active-Passive SimStereo - Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods. - Ziyu Jiang, Xuxi Chen, Xueqin Huang, Xianzhi Du, Denny Zhou, Zhangyang Wang:
Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation. - Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt:
Patching open-vocabulary models by interpolating weights. - Wayne Soo, Máté Lengyel:
Training stochastic stabilized supralinear networks by dynamics-neutral growth. - Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Post-hoc estimators for learning to defer to an expert. - Xin Wei, Xiang Gu, Jian Sun:
Learning Generalizable Part-based Feature Representation for 3D Point Clouds. - Tan Nguyen, Minh Pham, Tam Nguyen, Khai Nguyen, Stanley J. Osher, Nhat Ho:
FourierFormer: Transformer Meets Generalized Fourier Integral Theorem. - Lei Li, Nicolas Donati, Maks Ovsjanikov:
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching. - Han Yue, Chunhui Zhang, Chuxu Zhang, Hongfu Liu:
Label-invariant Augmentation for Semi-Supervised Graph Classification. - Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Practical Adversarial Multivalid Conformal Prediction. - Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei A. Efros:
Test-Time Training with Masked Autoencoders. - Andreas Besginow, Markus Lange-Hegermann:
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations. - J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller:
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems. - Aibek Alanov, Vadim Titov, Dmitry P. Vetrov:
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks. - Wenhan Huang, Kai Li, Kun Shao, Tianze Zhou, Matthew E. Taylor, Jun Luo, Dongge Wang, Hangyu Mao, Jianye Hao, Jun Wang, Xiaotie Deng:
Multiagent Q-learning with Sub-Team Coordination. - Tejas Srinivasan, Ting-Yun Chang, Leticia Leonor Pinto Alva, Georgios Chochlakis, Mohammad Rostami, Jesse Thomason:
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks. - Can Chen, Yingxue Zhang, Jie Fu, Xue (Steve) Liu, Mark Coates:
Bidirectional Learning for Offline Infinite-width Model-based Optimization. - Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni:
Differentially Private Model Compression. - Chao Tu, Yu Zhang, Zhenyuan Ning:
Dual-Curriculum Contrastive Multi-Instance Learning for Cancer Prognosis Analysis with Whole Slide Images. - Delvin Ce Zhang, Hady W. Lauw:
Meta-Complementing the Semantics of Short Texts in Neural Topic Models. - Wanxing Chang, Ye Shi, Hoang Tuan, Jingya Wang:
Unified Optimal Transport Framework for Universal Domain Adaptation. - Wenbo Su, Yuanxing Zhang, Yufeng Cai, Kaixu Ren, Pengjie Wang, Huimin Yi, Yue Song, Jing Chen, Hongbo Deng, Jian Xu, Lin Qu, Bo Zheng:
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models. - Yuhe Jin, Weiwei Sun, Jan Hosang, Eduard Trulls, Kwang Moo Yi:
TUSK: Task-Agnostic Unsupervised Keypoints. - Quanqi Hu, Yongjian Zhong, Tianbao Yang:
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. - Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao:
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering. - Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence H. Staib, James S. Duncan:
Class-Aware Adversarial Transformers for Medical Image Segmentation. - Jinyoung Choi, Bohyung Han:
MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators. - Dong Hoon Lee, Sungik Choi, Hyunwoo J. Kim, Sae-Young Chung:
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment. - Sikun Lin, Thomas Sprague, Ambuj K. Singh:
Mind Reader: Reconstructing complex images from brain activities. - Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster:
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. - Jogendra Nath Kundu, Suvaansh Bhambri, Akshay R. Kulkarni, Hiran Sarkar, Varun Jampani, Venkatesh Babu R.:
Subsidiary Prototype Alignment for Universal Domain Adaptation. - Zoe Ashwood, Aditi Jha, Jonathan W. Pillow:
Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior. - Samiul Alam, Luyang Liu, Ming Yan, Mi Zhang:
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction. - Liang Zhang, Anwen Hu, Qin Jin:
Multi-Lingual Acquisition on Multimodal Pre-training for Cross-modal Retrieval. - Guillaume Huguet, Daniel Sumner Magruder, Alexander Tong, Oluwadamilola Fasina, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy:
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference. - Shen-Huan Lyu, Yi-Xiao He, Zhi-Hua Zhou:
Depth is More Powerful than Width with Prediction Concatenation in Deep Forest. - Scott C. Lowe, Robert Earle, Jason d'Eon, Thomas Trappenberg, Sageev Oore:
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators. - Xi Weng, Lei Huang, Lei Zhao, Rao Muhammad Anwer, Salman H. Khan, Fahad Shahbaz Khan:
An Investigation into Whitening Loss for Self-supervised Learning. - Chenxiao Yang, Qitian Wu, Junchi Yan:
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks. - Aimen Zerroug, Mohit Vaishnav, Julien Colin, Sebastian Musslick, Thomas Serre:
A Benchmark for Compositional Visual Reasoning. - Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan:
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network. - Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon:
Myriad: a real-world testbed to bridge trajectory optimization and deep learning. - Rafael Oliveira, Louis C. Tiao, Fabio T. Ramos:
Batch Bayesian optimisation via density-ratio estimation with guarantees. - Yufei Chen, Chao Shen, Yun Shen, Cong Wang, Yang Zhang:
Amplifying Membership Exposure via Data Poisoning. - Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, Baoyuan Wu:
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation. - Kimon Antonakopoulos, Ali Kavis, Volkan Cevher:
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods. - Amit Alfassy, Assaf Arbelle, Oshri Halimi, Sivan Harary, Roei Herzig, Eli Schwartz, Rameswar Panda, Michele Dolfi, Christoph Auer, Peter W. J. Staar, Kate Saenko, Rogério Feris, Leonid Karlinsky:
FETA: Towards Specializing Foundational Models for Expert Task Applications. - Lili Su, Jiaming Xu, Pengkun Yang:
Global Convergence of Federated Learning for Mixed Regression. - Jinsoo Yoo, Frank Wood:
BayesPCN: A Continually Learnable Predictive Coding Associative Memory. - Rafid Mahmood, James Lucas, José M. Álvarez, Sanja Fidler, Marc T. Law:
Optimizing Data Collection for Machine Learning. - Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh:
DP-PCA: Statistically Optimal and Differentially Private PCA. - Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. - Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou:
Adapting to Online Label Shift with Provable Guarantees. - Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, Liwei Wang:
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds. - Haoyang Li, Shimin Di, Lei Chen:
Revisiting Injective Attacks on Recommender Systems. - Ruipeng Zhang, Chenning Yu, Jingkai Chen, Chuchu Fan, Sicun Gao:
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding. - Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack W. Rae, Laurent Sifre:
An empirical analysis of compute-optimal large language model training. - Dong-Sig Han, Hyunseo Kim, Hyundo Lee, Je-Hwan Ryu, Byoung-Tak Zhang:
Robust Imitation via Mirror Descent Inverse Reinforcement Learning. - Pratyush Maini, Saurabh Garg, Zachary C. Lipton, J. Zico Kolter:
Characterizing Datapoints via Second-Split Forgetting. - Patrick O'Reilly, Andreas Bugler, Keshav Bhandari, Max Morrison, Bryan Pardo:
VoiceBlock: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models. - Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang:
A Theoretical View on Sparsely Activated Networks. - Shoji Toyota, Kenji Fukumizu:
Invariance Learning based on Label Hierarchy. - Saket Tiwari, George Konidaris:
Effects of Data Geometry in Early Deep Learning. - Niranjan Damera Venkata, Chiranjib Bhattacharyya:
When to Intervene: Learning Optimal Intervention Policies for Critical Events. - Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han:
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer. - Shivam Gupta, Jasper C. H. Lee, Eric Price, Paul Valiant:
Finite-Sample Maximum Likelihood Estimation of Location. - Tim Dockhorn, Arash Vahdat, Karsten Kreis:
GENIE: Higher-Order Denoising Diffusion Solvers. - Alexander Meinke, Julian Bitterwolf, Matthias Hein:
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free. - Wei Zhuo, Guang Tan:
Efficient Graph Similarity Computation with Alignment Regularization. - Raihan Seraj, Jivitesh Sharma, Ole-Christoffer Granmo:
Tsetlin Machine for Solving Contextual Bandit Problems. - Paolo Muratore, Sina Tafazoli, Eugenio Piasini, Alessandro Laio, Davide Zoccolan:
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks. - Christian Koke, Gitta Kutyniok:
Graph Scattering beyond Wavelet Shackles. - Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi:
Matryoshka Representation Learning. - Nicolò Felicioni, Maurizio Ferrari Dacrema, Marcello Restelli, Paolo Cremonesi:
Off-Policy Evaluation with Deficient Support Using Side Information. - Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare:
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. - Zenan Li, Qitian Wu, Fan Nie, Junchi Yan:
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs. - Peng Jin, Jinfa Huang, Fenglin Liu, Xian Wu, Shen Ge, Guoli Song, David A. Clifton, Jie Chen:
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations. - Yijun Tan, Kai Han, Kang Zhao, Xianzhi Yu, Zidong Du, Yunji Chen, Yunhe Wang, Jun Yao:
Accelerating Sparse Convolution with Column Vector-Wise Sparsity. - Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer:
GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale. - Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig:
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. - Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf, Ke Li:
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders. - Benjamin Bowman, Guido F. Montúfar:
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime. - Xi Ye, Greg Durrett:
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning. - Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon:
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations. - Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal:
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs. - Guillaume O. Berger, Monal Narasimhamurthy, Kandai Watanabe, Morteza Lahijanian, Sriram Sankaranarayanan:
An Algorithm for Learning Switched Linear Dynamics from Data. - DaeJin Jo, Sungwoong Kim, Daniel Wontae Nam, Taehwan Kwon, Seungeun Rho, Jongmin Kim, Donghoon Lee:
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward. - Daniele Grattarola, Pierre Vandergheynst:
Generalised Implicit Neural Representations. - Yash Akhauri, Juan Pablo Muñoz, Nilesh Jain, Ravi Iyer:
EZNAS: Evolving Zero-Cost Proxies For Neural Architecture Scoring. - Shiyun Lin, Yuze Han, Xiang Li, Zhihua Zhang:
Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. - Ron Fisher, Dan Garber:
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity. - Hyeokjun Kweon, Kuk-Jin Yoon:
Joint Learning of 2D-3D Weakly Supervised Semantic Segmentation. - Leo Kozachkov, Michaela Ennis, Jean-Jacques E. Slotine:
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks. - Yaoyu Zhu, Zhaofei Yu, Wei Fang, Xiaodong Xie, Tiejun Huang, Timothée Masquelier:
Training Spiking Neural Networks with Event-driven Backpropagation. - Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. - Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh:
Efficient Non-Parametric Optimizer Search for Diverse Tasks. - Ximeng Sun, Ping Hu, Kate Saenko:
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations. - Shivam Garg, Dimitris Tsipras, Percy Liang, Gregory Valiant:
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes. - Shangchen Zhou, Kelvin C. K. Chan, Chongyi Li, Chen Change Loy:
Towards Robust Blind Face Restoration with Codebook Lookup Transformer. - Ali Ayub, Carter Fendley:
Few-Shot Continual Active Learning by a Robot. - Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava:
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems. - Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark S. Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong:
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization. - Hao Xiong, Yangxiao Lu, Nicholas Ruozzi:
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs. - Georgios Amanatidis, Georgios Birmpas, Aris Filos-Ratsikas, Alexandros A. Voudouris:
Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond. - Shiro Takagi:
On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning. - David Bertoin, Adil Zouitine, Mehdi Zouitine, Emmanuel Rachelson:
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning. - Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Information-Theoretic Safe Exploration with Gaussian Processes. - Renxiong Liu, Yunzhang Zhu:
On the consistent estimation of optimal Receiver Operating Characteristic (ROC) curve. - Max Springer, MohammadTaghi Hajiaghayi, Debmalya Panigrahi, Mohammad Reza Khani:
Online Algorithms for the Santa Claus Problem. - Wanyun Cui, Xingran Chen:
Instance-based Learning for Knowledge Base Completion. - Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye:
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. - Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran:
On Optimal Learning Under Targeted Data Poisoning. - Hao Zhu, Piotr Koniusz:
Generalized Laplacian Eigenmaps. - Ziyang Song, Bo Yang:
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds. - Yitian Zhang, Yue Bai, Huan Wang, Yi Xu, Yun Fu:
Look More but Care Less in Video Recognition. - Byoungjip Kim, Sungik Choi, Dasol Hwang, Moontae Lee, Honglak Lee:
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching. - Li Yang, Jian Meng, Jae-sun Seo, Deliang Fan:
Get More at Once: Alternating Sparse Training with Gradient Correction. - Thomas Chau, Lukasz Dudziak, Hongkai Wen, Nicholas D. Lane, Mohamed S. Abdelfattah:
BLOX: Macro Neural Architecture Search Benchmark and Algorithms. - Xiang Li, Jinghuan Shang, Srijan Das, Michael S. Ryoo:
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels? - Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. - Hansheng Xue, Vaibhav Rajan, Yu Lin:
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction. - Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, Maximilian Thiessen:
Active Learning of Classifiers with Label and Seed Queries. - Yongwei Chen, Rui Chen, Jiabao Lei, Yabin Zhang, Kui Jia:
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition. - Keitaro Sakamoto, Issei Sato:
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective. - Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Xiaoshuai Sun, Rongrong Ji, Dacheng Tao:
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach. - Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. - Shiau Hong Lim, Ilyas Malik:
Distributional Reinforcement Learning for Risk-Sensitive Policies. - Emanuele Guidotti, Alfio Ferrara:
Text Classification with Born's Rule. - Yixuan Pei, Zhiwu Qing, Jun Cen, Xiang Wang, Shiwei Zhang, Yaxiong Wang, Mingqian Tang, Nong Sang, Xueming Qian:
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning. - Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Conformal Prediction with Temporal Quantile Adjustments. - Jinghuan Shang, Srijan Das, Michael S. Ryoo:
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space. - Mintong Kang, Linyi Li, Maurice Weber, Yang Liu, Ce Zhang, Bo Li:
Certifying Some Distributional Fairness with Subpopulation Decomposition. - Bo Liu, Xidong Feng, Jie Ren, Luo Mai, Rui Zhu, Haifeng Zhang, Jun Wang, Yaodong Yang:
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. - Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov:
Optimal Algorithms for Decentralized Stochastic Variational Inequalities. - Haoyuan Sun, Kwangjun Ahn, Christos Thrampoulidis, Navid Azizan:
Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently. - Yichen Wu, Long-Kai Huang, Ying Wei:
Adversarial Task Up-sampling for Meta-learning. - Yue-Ting Pan, Jing-Lun Chou, Chun-Shu Wei:
MAtt: A Manifold Attention Network for EEG Decoding. - Zheng Dong, Ke Xu, Ziheng Duan, Hujun Bao, Weiwei Xu, Rynson W. H. Lau:
Geometry-aware Two-scale PIFu Representation for Human Reconstruction. - Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata, Anjan Dutta:
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators. - Azam Ikram, Sarthak Chakraborty, Subrata Mitra, Shiv Kumar Saini, Saurabh Bagchi, Murat Kocaoglu:
Root Cause Analysis of Failures in Microservices through Causal Discovery. - Celestine Mendler-Dünner, Frances Ding, Yixin Wang:
Anticipating Performativity by Predicting from Predictions. - Ninh Pham, Tao Liu:
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search. - Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu:
Pre-Trained Language Models for Interactive Decision-Making. - Pouria Mahdavinia, Yuyang Deng, Haochuan Li, Mehrdad Mahdavi:
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems. - Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee:
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation. - Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. - Liam O'Carroll, Vaidehi Srinivas, Aravindan Vijayaraghavan:
The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound. - De-An Huang, Zhiding Yu, Anima Anandkumar:
MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training. - Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, Mingsheng Long:
Supported Policy Optimization for Offline Reinforcement Learning. - Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian J. McAuley:
Infinite Recommendation Networks: A Data-Centric Approach. - Arsène Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, Joumana Ghosn:
DDXPlus: A New Dataset For Automatic Medical Diagnosis. - Eduard Gorbunov, Marina Danilova, David Dobre, Pavel E. Dvurechenskii, Alexander V. Gasnikov, Gauthier Gidel:
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise. - Ting Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin, David J. Fleet, Geoffrey E. Hinton:
A Unified Sequence Interface for Vision Tasks. - Niclas Boehmer, Robert Bredereck, Edith Elkind, Piotr Faliszewski, Stanislaw Szufa:
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning. - Chen Liang, Wenguan Wang, Jiaxu Miao, Yi Yang:
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models. - Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron:
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. - Alexander Matt Turner, Prasad Tadepalli:
Parametrically Retargetable Decision-Makers Tend To Seek Power. - Yicong Peng, Yichao Yan, Shengqi Liu, Yuhao Cheng, Shanyan Guan, Bowen Pan, Guangtao Zhai, Xiaokang Yang:
CageNeRF: Cage-based Neural Radiance Field for Generalized 3D Deformation and Animation. - Dongmin Park, Yooju Shin, Jihwan Bang, Youngjun Lee, Hwanjun Song, Jae-Gil Lee:
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning. - Hao Zheng, Hui Lin, Rong Zhao, Luping Shi:
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention. - Haoyu Wang, Nan Wu, Hang Yang, Cong Hao, Pan Li:
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation. - Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson:
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. - Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni:
BagFlip: A Certified Defense Against Data Poisoning. - Alexandre Galashov, Joshua Scott Merel, Nicolas Heess:
Data augmentation for efficient learning from parametric experts. - Giangiacomo Mercatali, André Freitas, Vikas Garg:
Symmetry-induced Disentanglement on Graphs. - Muhammad Faaiz Taufiq, Jean-Francois Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet:
Conformal Off-Policy Prediction in Contextual Bandits. - Konstantinos E. Nikolakakis, Farzin Haddadpour, Dionysios S. Kalogerias, Amin Karbasi:
Black-Box Generalization: Stability of Zeroth-Order Learning. - Siddharth Reddy, Sergey Levine, Anca D. Dragan:
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization. - Yishi Xu, Dongsheng Wang, Bo Chen, Ruiying Lu, Zhibin Duan, Mingyuan Zhou:
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding. - Tao Yang, Yuwang Wang, Yan Lu, Nanning Zheng:
Visual Concepts Tokenization. - Qishi Dong, Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Tianyang Hu, Yongxin Yang, Sung-Ho Bae, Zhenguo Li:
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization. - Clément L. Canonne, Ilias Diakonikolas, Daniel Kane, Sihan Liu:
Nearly-Tight Bounds for Testing Histogram Distributions. - Huibin Ge, Xiaohu Zhao, Chuang Liu, Yulong Zeng, Qun Liu, Deyi Xiong:
TGEA 2.0: A Large-Scale Diagnostically Annotated Dataset with Benchmark Tasks for Text Generation of Pretrained Language Models. - Vo Nguyen Le Duy, Shogo Iwazaki, Ichiro Takeuchi:
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference. - Bar Mahpud, Or Sheffet:
A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem. - Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik, Yuejie Chi:
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression. - Michele Bevilacqua, Giuseppe Ottaviano, Patrick S. H. Lewis, Scott Yih, Sebastian Riedel, Fabio Petroni:
Autoregressive Search Engines: Generating Substrings as Document Identifiers. - Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala:
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. - Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu:
Are All Losses Created Equal: A Neural Collapse Perspective. - Anay Mehrotra, Nisheeth K. Vishnoi:
Fair Ranking with Noisy Protected Attributes. - Guangmo Tong:
Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations. - Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang:
Provable Benefit of Multitask Representation Learning in Reinforcement Learning. - Marcel Binz, Eric Schulz:
Modeling Human Exploration Through Resource-Rational Reinforcement Learning. - Tim Brooks, Janne Hellsten, Miika Aittala, Ting-Chun Wang, Timo Aila, Jaakko Lehtinen, Ming-Yu Liu, Alexei A. Efros, Tero Karras:
Generating Long Videos of Dynamic Scenes. - Tiantian Fang, Ruoyu Sun, Alexander G. Schwing:
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data. - Athresh Karanam, KrishnaTeja Killamsetty, Harsha Kokel, Rishabh K. Iyer:
ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift. - Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, Jörg Frohberg, Mario Sasko, Quentin Lhoest, Angelina McMillan-Major, Gérard Dupont, Stella Biderman, Anna Rogers, Loubna Ben Allal, Francesco De Toni, Giada Pistilli, Olivier Nguyen, Somaieh Nikpoor, Maraim Masoud, Pierre Colombo, Javier de la Rosa, Paulo Villegas, Tristan Thrush, Shayne Longpre, Sebastian Nagel, Leon Weber, Manuel Muñoz, Jian Zhu, Daniel van Strien, Zaid Alyafeai, Khalid Almubarak, Minh Chien Vu, Itziar Gonzalez-Dios, Aitor Soroa, Kyle Lo, Manan Dey, Pedro Ortiz Suarez, Aaron Gokaslan, Shamik Bose, David Ifeoluwa Adelani, Long Phan, Hieu Tran, Ian Yu, Suhas Pai, Jenny Chim, Violette Lepercq, Suzana Ilic, Margaret Mitchell, Alexandra Sasha Luccioni, Yacine Jernite:
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset. - Hong Chen, Xin Wang, Yue Liu, Yuwei Zhou, Chaoyu Guan, Wenwu Zhu:
Module-Aware Optimization for Auxiliary Learning. - Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, Sanja Fidler:
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images. - Zhaohan Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot:
BYOL-Explore: Exploration by Bootstrapped Prediction. - Davide Buffelli, Pietro Lió, Fabio Vandin:
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. - Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello, Concetto Spampinato, Simone Calderara:
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning. - Miao Zhang, Wei Huang, Bin Yang:
Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations. - Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani:
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. - Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham:
On the Stability and Scalability of Node Perturbation Learning. - Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert:
Unsupervised Learning of Group Invariant and Equivariant Representations. - Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. - Oussama Boussif, Yoshua Bengio, Loubna Benabbou, Dan Assouline:
MAgNet: Mesh Agnostic Neural PDE Solver. - Mingdong Wu, Fangwei Zhong, Yulong Xia, Hao Dong:
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification. - Abdel Ghani Labassi, Didier Chételat, Andrea Lodi:
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks. - Yichen Zhu, Ning Liu, Zhiyuan Xu, Xin Liu, Weibin Meng, Louis Wang, Zhicai Ou, Jian Tang:
Teach Less, Learn More: On the Undistillable Classes in Knowledge Distillation. - Xingyu Qu, Diyang Li, Xiaohan Zhao, Bin Gu:
GAGA: Deciphering Age-path of Generalized Self-paced Regularizer. - Chaoqi Yang, Cheng Qian, Navjot Singh, Cao (Danica) Xiao, M. Brandon Westover, Edgar Solomonik, Jimeng Sun:
ATD: Augmenting CP Tensor Decomposition by Self Supervision. - Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Richard Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas:
Towards Learning Universal Hyperparameter Optimizers with Transformers. - Ivan Marisca, Andrea Cini, Cesare Alippi:
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations. - Yaohua Wang, Fangyi Zhang, Ming Lin, Senzhang Wang, Xiuyu Sun, Rong Jin:
Robust Graph Structure Learning via Multiple Statistical Tests. - Juhong Min, Yucheng Zhao, Chong Luo, Minsu Cho:
Peripheral Vision Transformer. - Indradyumna Roy, Soumen Chakrabarti, Abir De:
Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks. - Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh:
Differentially Private Learning with Margin Guarantees. - Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, Yue Zhao:
ADBench: Anomaly Detection Benchmark. - Xingting Yao, Fanrong Li, Zitao Mo, Jian Cheng:
GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks. - Li Wang, Jie Yang, Weikai Chen, Xiaoxu Meng, Bo Yang, Jintao Li, Lin Gao:
HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies. - Jibang Wu, Weiran Shen, Fei Fang, Haifeng Xu:
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality. - Achraf Azize, Debabrota Basu:
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits. - Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh:
Robust Reinforcement Learning using Offline Data. - Takuya Ito, Tim Klinger, Douglas Schultz, John Murray, Michael W. Cole, Mattia Rigotti:
Compositional generalization through abstract representations in human and artificial neural networks. - Blake Bordelon, Cengiz Pehlevan:
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks. - Amauri H. Souza, Diego Mesquita, Samuel Kaski, Vikas Garg:
Provably expressive temporal graph networks. - Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon:
Maximum Likelihood Training of Implicit Nonlinear Diffusion Model. - Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar:
First is Better Than Last for Language Data Influence. - Yuesong Shen, Daniel Cremers:
Deep Combinatorial Aggregation. - Na Young Jun, Greg Field, John M. Pearson:
Efficient coding, channel capacity, and the emergence of retinal mosaics. - Xuefei Ning, Zixuan Zhou, Junbo Zhao, Tianchen Zhao, Yiping Deng, Changcheng Tang, Shuang Liang, Huazhong Yang, Yu Wang:
TA-GATES: An Encoding Scheme for Neural Network Architectures. - Arjun Majumdar, Gunjan Aggarwal, Bhavika Devnani, Judy Hoffman, Dhruv Batra:
ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings. - Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy:
Autoformalization with Large Language Models. - Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris:
Model-based Lifelong Reinforcement Learning with Bayesian Exploration. - Yichen Li, Chicheng Zhang:
On Efficient Online Imitation Learning via Classification. - Xu Yan, Heshen Zhan, Chaoda Zheng, Jiantao Gao, Ruimao Zhang, Shuguang Cui, Zhen Li:
Let Images Give You More: Point Cloud Cross-Modal Training for Shape Analysis. - Jingyuan Xu, Weiwei Liu:
On Robust Multiclass Learnability. - Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long:
Debiased Self-Training for Semi-Supervised Learning. - Yitian Hong, Yaochu Jin, Yang Tang:
Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning. - Omri Ben-Eliezer, Dan Mikulincer, Ilias Zadik:
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions. - Ruijia Wang, Xiao Wang, Chuan Shi, Le Song:
Uncovering the Structural Fairness in Graph Contrastive Learning. - Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten:
Estimation of Entropy in Constant Space with Improved Sample Complexity. - Yuichi Yoshida, Shinji Ito:
Average Sensitivity of Euclidean k-Clustering. - Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang:
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. - Qinzi Zhang, Hoang Tran, Ashok Cutkosky:
Differentially Private Online-to-batch for Smooth Losses. - Junjie Chen, Li Niu, Siyuan Zhou, Jianlou Si, Chen Qian, Liqing Zhang:
Weak-shot Semantic Segmentation via Dual Similarity Transfer. - Wonseok Hwang, Dongjun Lee, Kyoungyeon Cho, Hanuhl Lee, Minjoon Seo:
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction. - Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang:
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. - Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni B. Chan, Rong Jin:
Improved Fine-Tuning by Better Leveraging Pre-Training Data. - Bang An, Zora Che, Mucong Ding, Furong Huang:
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. - Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, Ziwei Liu:
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. - Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. - Antonin Schrab, Benjamin Guedj, Arthur Gretton:
KSD Aggregated Goodness-of-fit Test. - Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Efficient Risk-Averse Reinforcement Learning. - Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole, Cengiz Öztireli:
D^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video. - Itay Safran, Gal Vardi, Jason D. Lee:
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. - Chin Pang Ho, Marek Petrik, Wolfram Wiesemann:
Robust $\phi$-Divergence MDPs. - Michael Chang, Tom Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. - Alireza Fathollah Pour, Hassan Ashtiani:
Benefits of Additive Noise in Composing Classes with Bounded Capacity. - Weiyu Chen, James Kwok:
Multi-Objective Deep Learning with Adaptive Reference Vectors. - Zaiyu Huang, Hanhui Li, Zhenyu Xie, Michael Kampffmeyer, Qingling Cai, Xiaodan Liang:
Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning. - Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu:
Pushing the limits of fairness impossibility: Who's the fairest of them all? - Aidan Good, Jiaqi Lin, Xin Yu, Hannah Sieg, Mikey Ferguson, Shandian Zhe, Jerzy Wieczorek, Thiago Serra:
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm. - Ali Seyfi, Jean-François Rajotte, Raymond T. Ng:
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN). - Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu:
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder. - Xingang Guo, Bin Hu:
Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential. - Henry Kvinge, Tegan Emerson, Grayson Jorgenson, Scott Vasquez, Tim Doster, Jesse D. Lew:
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? - Kasper Green Larsen, Martin Ritzert:
Optimal Weak to Strong Learning. - Seo Taek Kong, Soomin Jeon, Dongbin Na, Jaewon Lee, Hong-Seok Lee, Kyu-Hwan Jung:
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity. - Marius Dragoi, Elena Burceanu, Emanuela Haller, Andrei Manolache, Florin Brad:
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection. - Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster:
Grounding Aleatoric Uncertainty for Unsupervised Environment Design. - Omar Shouman, Wassim Gabriel, Victor-George Giurcoiu, Vitor Sternlicht, Mathias Wilhelm:
PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics. - Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, Furu Wei:
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts. - Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, Mingsheng Long:
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models. - Farimah Poursafaei, Shenyang Huang, Kellin Pelrine, Reihaneh Rabbany:
Towards Better Evaluation for Dynamic Link Prediction. - Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, Jianfeng Gao, Lijuan Wang:
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone. - Arjun Subramonian, Kai-Wei Chang, Yizhou Sun:
On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs. - Che Wang, Xufang Luo, Keith W. Ross, Dongsheng Li:
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning. - Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Efficient and Effective Optimal Transport-Based Biclustering. - Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low:
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. - Bo Xiong, Michael Cochez, Mojtaba Nayyeri, Steffen Staab:
Hyperbolic Embedding Inference for Structured Multi-Label Prediction. - Christopher Grimm, André Barreto, Satinder Singh:
Approximate Value Equivalence. - Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. - Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha:
Rank Diminishing in Deep Neural Networks. - Sepehr Assadi, Chen Wang:
Single-pass Streaming Lower Bounds for Multi-armed Bandits Exploration with Instance-sensitive Sample Complexity. - Cédric Malherbe, Antoine Grosnit, Rasul Tutunov, Haitham Bou-Ammar, Jun Wang:
Optimistic Tree Searches for Combinatorial Black-Box Optimization. - Stephen Casper, Max Nadeau, Dylan Hadfield-Menell, Gabriel Kreiman:
Robust Feature-Level Adversaries are Interpretability Tools. - Xiao Li, Andre Milzarek:
A Unified Convergence Theorem for Stochastic Optimization Methods. - Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora:
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. - Aaron Zweig, Joan Bruna:
Exponential Separations in Symmetric Neural Networks. - Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann:
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. - Tianhao Chu, Zilong Ji, Junfeng Zuo, Wenhao Zhang, Tiejun Huang, Yuanyuan Mi, Si Wu:
Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells. - Hyunchai Jeong, Jin Tian, Elias Bareinboim:
Finding and Listing Front-door Adjustment Sets. - Guobing Gan, Peng Zhang, Sunzhu Li, Xiuqing Lu, Benyou Wang:
MorphTE: Injecting Morphology in Tensorized Embeddings. - Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. - Lars Schmarje, Vasco Grossmann, Claudius Zelenka, Sabine Dippel, Rainer Kiko, Mariusz Oszust, Matti Pastell, Jenny Stracke, Anna Valros, Nina Volkmann, Reinhard Koch:
Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation. - Yiping Lu, José H. Blanchet, Lexing Ying:
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. - DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur:
Block-Recurrent Transformers. - Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. - Sejong Yang, Subin Jeon, Seonghyeon Nam, Seon Joo Kim:
Dense Interspecies Face Embedding. - Byungchan Ko, Jungseul Ok:
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning. - Chaoqi Chen, Luyao Tang, Feng Liu, Gangming Zhao, Yue Huang, Yizhou Yu:
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization. - Pouya M. Ghari, Yanning Shen:
Personalized Online Federated Learning with Multiple Kernels. - Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao:
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. - Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael W. Sjoding, Jenna Wiens:
Learning Concept Credible Models for Mitigating Shortcuts. - Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen:
Neural Approximation of Graph Topological Features. - Adam Bielski, Paolo Favaro:
MOVE: Unsupervised Movable Object Segmentation and Detection. - Mengping Yang, Zhe Wang, Ziqiu Chi, Yanbing Zhang:
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data. - Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun:
LBD: Decouple Relevance and Observation for Individual-Level Unbiased Learning to Rank. - Jiexi Yan, Erkun Yang, Cheng Deng, Heng Huang:
MetricFormer: A Unified Perspective of Correlation Exploring in Similarity Learning. - Juliette Millet, Charlotte Caucheteux, Pierre Orhan, Yves Boubenec, Alexandre Gramfort, Ewan Dunbar, Christophe Pallier, Jean-Remi King:
Toward a realistic model of speech processing in the brain with self-supervised learning. - Shicheng Liu, Minghui Zhu:
Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems. - Dylan M. Asmar, Mykel J. Kochenderfer:
Collaborative Decision Making Using Action Suggestions. - Tiancheng Jin, Tal Lancewicki, Haipeng Luo, Yishay Mansour, Aviv Rosenberg:
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback. - Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire:
Provably sample-efficient RL with side information about latent dynamics. - Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich, Alexander V. Gasnikov, Gesualdo Scutari:
Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity. - Matthew Morris, Thomas D. Barrett, Arnu Pretorius:
Universally Expressive Communication in Multi-Agent Reinforcement Learning. - Benjamin Dubois-Taine, Francis R. Bach, Quentin Berthet, Adrien B. Taylor:
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization. - Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, Lequan Yu:
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning. - Wenbo Zhang, Likai Tang, Site Mo, Xianggen Liu, Sen Song:
Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences. - Sérgio M. Jesus, José Pombal, Duarte Alves, André Ferreira Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro:
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. - Qilong Wang, Mingze Gao, Zhaolin Zhang, Jiangtao Xie, Peihua Li, Qinghua Hu:
DropCov: A Simple yet Effective Method for Improving Deep Architectures. - Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Giulia Romano, Nicola Gatti:
A Unifying Framework for Online Optimization with Long-Term Constraints. - Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for local learning at equilibrium. - Hila Chefer, Idan Schwartz, Lior Wolf:
Optimizing Relevance Maps of Vision Transformers Improves Robustness. - Weixuan Liang, Xinwang Liu, Yong Liu, Sihang Zhou, Jun-Jie Huang, Siwei Wang, Jiyuan Liu, Yi Zhang, En Zhu:
Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel. - Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham:
Deep Ensembles Work, But Are They Necessary? - Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira:
VICE: Variational Interpretable Concept Embeddings. - Minjing Dong, Xinghao Chen, Yunhe Wang, Chang Xu:
Random Normalization Aggregation for Adversarial Defense. - Jiashuo Liu, Jiayun Wu, Bo Li, Peng Cui:
Distributionally Robust Optimization with Data Geometry. - Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski:
Near-Optimal Correlation Clustering with Privacy. - Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu:
Knowledge Distillation from A Stronger Teacher. - Maarten Buyl, Tijl De Bie:
Optimal Transport of Classifiers to Fairness. - Antonio Montanaro, Diego Valsesia, Enrico Magli:
Rethinking the compositionality of point clouds through regularization in the hyperbolic space. - Gyungin Shin, Weidi Xie, Samuel Albanie:
ReCo: Retrieve and Co-segment for Zero-shot Transfer. - Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, Angjoo Kanazawa:
Monocular Dynamic View Synthesis: A Reality Check. - Hanoona Abdul Rasheed, Muhammad Maaz, Muhammad Uzair Khattak, Salman H. Khan, Fahad Shahbaz Khan:
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection. - David Simchi-Levi, Zeyu Zheng, Feng Zhu:
A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk. - Nataly Brukhim, Elad Hazan, Karan Singh:
A Boosting Approach to Reinforcement Learning. - Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk:
Relaxing Equivariance Constraints with Non-stationary Continuous Filters. - Afonso S. Bandeira, Ahmed El Alaoui, Samuel B. Hopkins, Tselil Schramm, Alexander S. Wein, Ilias Zadik:
The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics. - Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian M. Schmon:
Robust Neural Posterior Estimation and Statistical Model Criticism. - Changyou Chen, Jianyi Zhang, Yi Xu, Liqun Chen, Jiali Duan, Yiran Chen, Son Tran, Belinda Zeng, Trishul Chilimbi:
Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective. - Ruisi Cai, Zhenyu Zhang, Tianlong Chen, Xiaohan Chen, Zhangyang Wang:
Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets. - Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik:
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts. - Yang Cai, Argyris Oikonomou, Weiqiang Zheng:
Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games. - Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
When are Local Queries Useful for Robust Learning? - Sheng-Yu Huang, Hao-Yu Hsu, Yu-Chiang Frank Wang:
SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion. - Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette:
Improving Intrinsic Exploration with Language Abstractions. - Aditya Desai, Anshumali Shrivastava:
The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model. - Liyu Chen, Haipeng Luo:
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments. - Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen:
A Unified Framework for Deep Symbolic Regression. - Katja Schwarz, Axel Sauer, Michael Niemeyer, Yiyi Liao, Andreas Geiger:
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids. - Erchuan Zhang, David Suter, Giang Truong, Syed Zulqarnain Gilani:
Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model. - Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li:
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. - Mira Finkelstein, Nitsan Levy Schlot, Lucy Liu, Yoav Kolumbus, David C. Parkes, Jeffrey S. Rosenschein, Sarah Keren:
Explainable Reinforcement Learning via Model Transforms. - Zhi-Hao Tan, Yi Xie, Yuan Jiang, Zhi-Hua Zhou:
Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks. - Jianghong Shi, Eric Shea-Brown, Michael A. Buice:
Learning dynamics of deep linear networks with multiple pathways. - Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng:
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition. - Xin Liu, Zhongdao Wang, Yali Li, Shengjin Wang:
Self-Supervised Learning via Maximum Entropy Coding. - Lingxiao Zhao, Neil Shah, Leman Akoglu:
A Practical, Progressively-Expressive GNN. - Changjian Shui, Gezheng Xu, Qi Chen, Jiaqi Li, Charles X. Ling, Tal Arbel, Boyu Wang, Christian Gagné:
On Learning Fairness and Accuracy on Multiple Subgroups. - Shuai Jia, Bangjie Yin, Taiping Yao, Shouhong Ding, Chunhua Shen, Xiaokang Yang, Chao Ma:
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition. - Daniel Vera Nieto, Luigi Celona, Clara Fernandez-Labrador:
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment. - Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh:
Structuring Representations Using Group Invariants. - Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos:
Black-box coreset variational inference. - Yu Yang, Xiaotian Cheng, Chang Liu, Hakan Bilen, Xiangyang Ji:
Distilling Representations from GAN Generator via Squeeze and Span. - Zi Wang, Gautam Prakriya, Somesh Jha:
A Quantitative Geometric Approach to Neural-Network Smoothness. - Lijun Zhang, Xiao Liu, Hui Guan:
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning. - Jordan Awan, Jinshuo Dong:
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy. - Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka:
On the generalization of learning algorithms that do not converge. - Onur Beker, Mohammad Mohammadi, Amir Zamir:
PALMER: Perception - Action Loop with Memory for Long-Horizon Planning. - Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. - Mohammad Reza Taesiri, Giang Nguyen, Anh Nguyen:
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy. - Jan Schuchardt, Stephan Günnemann:
Invariance-Aware Randomized Smoothing Certificates. - Marcus Nordström, Henrik Hult, Fredrik Löfman, Jonas Söderberg:
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice. - Jianhao Ma, Salar Fattahi:
Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution. - Guanhua Zhang, Yihua Zhang, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, Shiyu Chang:
Fairness Reprogramming. - Yongchan Kwon, James Y. Zou:
WeightedSHAP: analyzing and improving Shapley based feature attributions. - Chaoteng Duan, Jianhao Ding, Shiyan Chen, Zhaofei Yu, Tiejun Huang:
Temporal Effective Batch Normalization in Spiking Neural Networks. - Zhiwei Deng, Olga Russakovsky:
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks. - Madeline Schiappa, Shruti Vyas, Hamid Palangi, Yogesh S. Rawat, Vibhav Vineet:
Robustness Analysis of Video-Language Models Against Visual and Language Perturbations. - Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi:
Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds. - Yuchen Rao, Yinyu Nie, Angela Dai:
PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories. - Yanjing Li, Sheng Xu, Baochang Zhang, Xianbin Cao, Peng Gao, Guodong Guo:
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer. - Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar:
Enhancing Safe Exploration Using Safety State Augmentation. - Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel:
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control. - Zhipeng Tu, Xi Wang, Yiguang Hong, Lei Wang, Deming Yuan, Guodong Shi:
Distributed Online Convex Optimization with Compressed Communication. - Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner:
Local Latent Space Bayesian Optimization over Structured Inputs. - Parnian Kassraie, Andreas Krause, Ilija Bogunovic:
Graph Neural Network Bandits. - Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon:
Concrete Score Matching: Generalized Score Matching for Discrete Data. - Sihan Zeng, Thinh T. Doan, Justin Romberg:
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games. - Valerii Likhosherstov, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller:
Chefs' Random Tables: Non-Trigonometric Random Features. - Stephanie Schoch, Haifeng Xu, Yangfeng Ji:
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification. - Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pascale Fung, Mohammad Shoeybi, Bryan Catanzaro:
Factuality Enhanced Language Models for Open-Ended Text Generation. - Zewen Chi, Li Dong, Shaohan Huang, Damai Dai, Shuming Ma, Barun Patra, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei:
On the Representation Collapse of Sparse Mixture of Experts. - Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. - Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora:
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. - Siqiao Xue, Xiaoming Shi, James Y. Zhang, Hongyuan Mei:
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences. - Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams:
Towards Understanding Grokking: An Effective Theory of Representation Learning. - Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang:
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. - Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed:
Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference. - Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora:
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. - Yijia Zheng, Tong He, Yixuan Qiu, David P. Wipf:
Learning Manifold Dimensions with Conditional Variational Autoencoders. - Shentong Mo, Yapeng Tian:
Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing. - Dmitry Ivanov, Iskander Safiulin, Igor Filippov, Ksenia Balabaeva:
Optimal-er Auctions through Attention. - Kerong Wang, Hanye Zhao, Xufang Luo, Kan Ren, Weinan Zhang, Dongsheng Li:
Bootstrapped Transformer for Offline Reinforcement Learning. - Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Tom Griffiths, Dylan Hadfield-Menell:
How to talk so AI will learn: Instructions, descriptions, and autonomy. - Emmanuel Esposito, Federico Fusco, Dirk van der Hoeven, Nicolò Cesa-Bianchi:
Learning on the Edge: Online Learning with Stochastic Feedback Graphs. - Qianyi Li, Haim Sompolinsky:
Globally Gated Deep Linear Networks. - Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim:
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. - Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang, Kai Zheng, Enhong Chen:
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever. - Kwangho Kim, Edward H. Kennedy, José R. Zubizarreta:
Doubly Robust Counterfactual Classification. - Seungyong Moon, JunYeong Lee, Hyun Oh Song:
Rethinking Value Function Learning for Generalization in Reinforcement Learning. - Zhuolin Yang, Zhikuan Zhao, Boxin Wang, Jiawei Zhang, Linyi Li, Hengzhi Pei, Bojan Karlas, Ji Liu, Heng Guo, Ce Zhang, Bo Li:
Improving Certified Robustness via Statistical Learning with Logical Reasoning. - Yaodong Yang, Guangyong Chen, Weixun Wang, Xiaotian Hao, Jianye Hao, Pheng-Ann Heng:
Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing. - Gregory Kehne, Ariel D. Procaccia, Jingyan Wang:
Recruitment Strategies That Take a Chance. - Zhi Tian, Xiangxiang Chu, Xiaoming Wang, Xiaolin Wei, Chunhua Shen:
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images. - Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Heather Zheng, Prateek Mittal:
Understanding Robust Learning through the Lens of Representation Similarities. - Kiarash Shamsi, Friedhelm Victor, Murat Kantarcioglu, Yulia R. Gel, Cuneyt Gurcan Akcora:
Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains. - Weijia Zhang, Xuanhui Zhang, Hanwen Deng, Min-Ling Zhang:
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization. - Guan Yang, Minghuan Liu, Weijun Hong, Weinan Zhang, Fei Fang, Guangjun Zeng, Yue Lin:
PerfectDou: Dominating DouDizhu with Perfect Information Distillation. - Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. - Emily Wenger, Mingjie Chen, François Charton, Kristin E. Lauter:
SALSA: Attacking Lattice Cryptography with Transformers. - Santiago Cuervo, Adrian Lancucki, Ricard Marxer, Pawel Rychlikowski, Jan Chorowski:
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech. - Henger Li, Xiaolin Sun, Zizhan Zheng:
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework. - Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian:
Learning Neural Set Functions Under the Optimal Subset Oracle. - Chloé Rouyer, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Yevgeny Seldin:
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs. - David P. Woodruff, Fred Zhang, Richard Zhang:
Optimal Query Complexities for Dynamic Trace Estimation. - John R. Birge, Xiaocheng Li, Chunlin Sun:
Learning from Stochastically Revealed Preference. - Jin-Hwa Kim, Yunji Kim, Jiyoung Lee, Kang Min Yoo, Sang-Woo Lee:
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models. - Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, Yixuan Li:
Delving into Out-of-Distribution Detection with Vision-Language Representations. - Xingyi He, Jiaming Sun, Yuang Wang, Di Huang, Hujun Bao, Xiaowei Zhou:
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models. - Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu:
Confidence-based Reliable Learning under Dual Noises. - Giannis Daras, Negin Raoof, Zoi Gkalitsiou, Alex Dimakis:
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve. - Sitan Chen, Jerry Li, Yuanzhi Li:
Learning (Very) Simple Generative Models Is Hard. - Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang:
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding. - Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. - Dirk van der Hoeven, Nikita Zhivotovskiy, Nicolò Cesa-Bianchi:
A Regret-Variance Trade-Off in Online Learning. - Andre Wibisono, Molei Tao, Georgios Piliouras:
Alternating Mirror Descent for Constrained Min-Max Games. - Edward De Brouwer:
Deep Counterfactual Estimation with Categorical Background Variables. - Jose Pablo Folch, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener:
SnAKe: Bayesian Optimization with Pathwise Exploration. - Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan:
Self-Supervised Learning with an Information Maximization Criterion. - Shangbin Feng, Zhaoxuan Tan, Herun Wan, Ningnan Wang, Zilong Chen, Binchi Zhang, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, Xinshun Feng, Qingyue Zhang, Hongrui Wang, Yuhan Liu, Yuyang Bai, Heng Wang, Zijian Cai, Yanbo Wang, Lijing Zheng, Zihan Ma, Jundong Li, Minnan Luo:
TwiBot-22: Towards Graph-Based Twitter Bot Detection. - Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard:
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. - Alekh Agarwal, Tong Zhang:
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. - Wuyang Chen, Wei Huang, Xinyu Gong, Boris Hanin, Zhangyang Wang:
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis. - Wanhua Li, Xiaoke Huang, Zheng Zhu, Yansong Tang, Xiu Li, Jie Zhou, Jiwen Lu:
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression. - Georgios Iatropoulos, Johanni Brea, Wulfram Gerstner:
Kernel Memory Networks: A Unifying Framework for Memory Modeling. - Dmitry Kovalev, Alexander V. Gasnikov:
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization. - Alp Yurtsever, Suvrit Sra:
CCCP is Frank-Wolfe in disguise. - Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew J. Hausknecht, Anca D. Dragan, Sam Devlin:
Uni[MASK]: Unified Inference in Sequential Decision Problems. - Yinglun Zhu, Robert Nowak:
Efficient Active Learning with Abstention. - Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, Kevin Bailly:
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance. - Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. - Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd, Ching-An Cheng, Matthew J. Hausknecht:
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control. - Renata Turkes, Guido F. Montúfar, Nina Otter:
On the Effectiveness of Persistent Homology. - Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu:
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context. - Weicong Liang, Yuhui Yuan, Henghui Ding, Xiao Luo, Weihong Lin, Ding Jia, Zheng Zhang, Chao Zhang, Han Hu:
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning. - Bálint Máté, Samuel Klein, Tobias Golling, François Fleuret:
Flowification: Everything is a normalizing flow. - Rainer Engelken, Sven Goedeke:
A time-resolved theory of information encoding in recurrent neural networks. - Chanwoo Park, Sangdoo Yun, Sanghyuk Chun:
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective. - Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak:
Recursive Reinforcement Learning. - Yiren Jian, Chongyang Gao, Soroush Vosoughi:
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings. - Minyi Zhao, Bingjia Li, Jie Wang, Wanqing Li, Wenjing Zhou, Lan Zhang, Shijie Xuyang, Zhihang Yu, Xinkun Yu, Guangze Li, Aobotao Dai, Shuigeng Zhou:
Towards Video Text Visual Question Answering: Benchmark and Baseline. - Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang:
4D Unsupervised Object Discovery. - Piotr Indyk, Sandeep Silwal:
Faster Linear Algebra for Distance Matrices. - Zhen Li, Gilles Stoltz:
Contextual Bandits with Knapsacks for a Conversion Model. - Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov:
Contrastive Learning as Goal-Conditioned Reinforcement Learning. - Gaurav Manek, J. Zico Kolter:
The Pitfalls of Regularization in Off-Policy TD Learning. - Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, Yu Qiao:
MCMAE: Masked Convolution Meets Masked Autoencoders. - Diane Oyen, Michal Kucer, Nicolas W. Hengartner, Har Simrat Singh:
Robustness to Label Noise Depends on the Shape of the Noise Distribution. - Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi:
Fast Neural Kernel Embeddings for General Activations. - Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
Deep invariant networks with differentiable augmentation layers. - Wonyong Jeong, Sung Ju Hwang:
Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching. - Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal:
Reinforcement Learning with a Terminator. - Yo-whan Kim, Samarth Mishra, SouYoung Jin, Rameswar Panda, Hilde Kuehne, Leonid Karlinsky, Venkatesh Saligrama, Kate Saenko, Aude Oliva, Rogério Feris:
How Transferable are Video Representations Based on Synthetic Data? - Aaron Potechin, Goutham Rajendran:
Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis. - Ville Hyvönen, Elias Jääsaari, Teemu Roos:
A Multilabel Classification Framework for Approximate Nearest Neighbor Search. - Renan A. Rojas-Gomez, Teck-Yian Lim, Alexander G. Schwing, Minh N. Do, Raymond A. Yeh:
Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks. - Pedro Savarese, Xin Yuan, Yanjing Li, Michael Maire:
Not All Bits have Equal Value: Heterogeneous Precisions via Trainable Noise. - Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, Tianqi Chen:
Tensor Program Optimization with Probabilistic Programs. - Shiyu Wang, Xiaojie Guo, Liang Zhao:
Deep Generative Model for Periodic Graphs. - Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro:
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models. - Fengzhuo Zhang, Boyi Liu, Kaixin Wang, Vincent Y. F. Tan, Zhuoran Yang, Zhaoran Wang:
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL. - Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato:
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. - Jakiw Pidstrigach:
Score-Based Generative Models Detect Manifolds. - Yair Carmon, Danielle Hausler:
Distributionally Robust Optimization via Ball Oracle Acceleration. - Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh:
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. - Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre L. Dognin, Kush R. Varshney:
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting. - Drew Prinster, Anqi Liu, Suchi Saria:
JAWS: Auditing Predictive Uncertainty Under Covariate Shift. - Matthew Aitchison, Penny Sweetser:
DNA: Proximal Policy Optimization with a Dual Network Architecture. - Robert B. Gramacy, Annie Sauer, Nathan Wycoff:
Triangulation candidates for Bayesian optimization. - Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, Kaiming He:
Masked Autoencoders As Spatiotemporal Learners. - Yuting Gao, Jinfeng Liu, Zihan Xu, Jun Zhang, Ke Li, Rongrong Ji, Chunhua Shen:
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining. - Albert Gu, Karan Goel, Ankit Gupta, Christopher Ré:
On the Parameterization and Initialization of Diagonal State Space Models. - Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington:
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. - Hengguan Huang, Xiangming Gu, Hao Wang, Chang Xiao, Hongfu Liu, Ye Wang:
Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation. - Vivak Patel, Shushu Zhang, Bowen Tian:
Global Convergence and Stability of Stochastic Gradient Descent. - Haotao Wang, Junyuan Hong, Aston Zhang, Jiayu Zhou, Zhangyang Wang:
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork. - Grigoris Velegkas, Zhuoran Yang, Amin Karbasi:
Reinforcement Learning with Logarithmic Regret and Policy Switches. - Minchul Kim, Feng Liu, Anil K. Jain, Xiaoming Liu:
Cluster and Aggregate: Face Recognition with Large Probe Set. - Haotian Zhang, Pengchuan Zhang, Xiaowei Hu, Yen-Chun Chen, Liunian Harold Li, Xiyang Dai, Lijuan Wang, Lu Yuan, Jenq-Neng Hwang, Jianfeng Gao:
GLIPv2: Unifying Localization and Vision-Language Understanding. - Shuwei Shi, Jinjin Gu, Liangbin Xie, Xintao Wang, Yujiu Yang, Chao Dong:
Rethinking Alignment in Video Super-Resolution Transformers. - Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization). - Patrick Fernandes, Marcos V. Treviso, Danish Pruthi, André F. T. Martins, Graham Neubig:
Learning to Scaffold: Optimizing Model Explanations for Teaching. - Xingzhe He, Bastian Wandt, Helge Rhodin:
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints. - Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar:
Composite Feature Selection Using Deep Ensembles. - Junru Wu, Yi Liang, Feng Han, Hassan Akbari, Zhangyang Wang, Cong Yu:
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization. - Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, Adam Wierman:
Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity. - Yue Wu, Yu Deng, Jiaolong Yang, Fangyun Wei, Qifeng Chen, Xin Tong:
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars. - Gokul NC, Manideep Ladi, Sumit Negi, Prem Selvaraj, Pratyush Kumar, Mitesh M. Khapra:
Addressing Resource Scarcity across Sign Languages with Multilingual Pretraining and Unified-Vocabulary Datasets. - Lizhen Nie, Dan Nicolae:
Detection and Localization of Changes in Conditional Distributions. - Xuan-Phi Nguyen, Shafiq R. Joty, Kui Wu, Ai Ti Aw:
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model. - Weichao Mao, Haoran Qiu, Chen Wang, Hubertus Franke, Zbigniew Kalbarczyk, Ravishankar K. Iyer, Tamer Basar:
A Mean-Field Game Approach to Cloud Resource Management with Function Approximation. - Osbert Bastani, Yecheng Jason Ma, Estelle Shen, Wanqiao Xu:
Regret Bounds for Risk-Sensitive Reinforcement Learning. - Xuelong Mi, Mengfan Wang, Alex Chen, Jing-Xuan Lim, Yizhi Wang, Misha B. Ahrens, Guoqiang Yu:
BILCO: An Efficient Algorithm for Joint Alignment of Time Series. - Evan Zheran Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn:
Giving Feedback on Interactive Student Programs with Meta-Exploration. - Dieqiao Feng, Carla P. Gomes, Bart Selman:
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning. - Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko, Maxim Panov:
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network. - Zongxin Yang, Yi Yang:
Decoupling Features in Hierarchical Propagation for Video Object Segmentation. - Dongruo Zhou, Quanquan Gu:
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. - Jonathan G. Richens, Rory Beard, Daniel H. Thompson:
Counterfactual harm. - Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Chain of Thought Imitation with Procedure Cloning. - Kun Yuan, Xinmeng Huang, Yiming Chen, Xiaohan Zhang, Yingya Zhang, Pan Pan:
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization. - Zhenting Wang, Hailun Ding, Juan Zhai, Shiqing Ma:
Training with More Confidence: Mitigating Injected and Natural Backdoors During Training. - Yu Wang, Jingjing Zou, Jingyang Lin, Qing Ling, Yingwei Pan, Ting Yao, Tao Mei:
Out-of-Distribution Detection via Conditional Kernel Independence Model. - Hengquan Guo, Xin Liu, Honghao Wei, Lei Ying:
Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond. - Qinglong Zhang, Yu-Bin Yang:
ResT V2: Simpler, Faster and Stronger. - Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen:
Object-Category Aware Reinforcement Learning. - David W. Romero, Suhas Lohit:
Learning Partial Equivariances From Data. - Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, Mohammad Norouzi:
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. - Zichen Zhang, Jun Jin, Martin Jägersand, Jun Luo, Dale Schuurmans:
A Simple Decentralized Cross-Entropy Method. - Peirong Zhang, Jiajia Jiang, Yuliang Liu, Lianwen Jin:
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification. - Michael E. Sander, Pierre Ablin, Gabriel Peyré:
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? - Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu:
Diffusion-based Molecule Generation with Informative Prior Bridges. - Taro Makino, Krzysztof J. Geras, Kyunghyun Cho:
Generative multitask learning mitigates target-causing confounding. - Ruinan Jin, Xingkang He, Lang Chen, Difei Cheng, Vijay Gupta:
Revisit last-iterate convergence of mSGD under milder requirement on step size. - Jiyang Guan, Jian Liang, Ran He:
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks. - Tanguy Marchand, Boris Muzellec, Constance Beguier, Jean Ogier du Terrail, Mathieu Andreux:
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning. - Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, Xianyuan Zhan:
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning. - Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien:
Data-Efficient Structured Pruning via Submodular Optimization. - Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Interpolation and Regularization for Causal Learning. - Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng:
A Direct Approximation of AIXI Using Logical State Abstractions. - Jianda Chen, Sinno Jialin Pan:
Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning. - Luo Luo, Yujun Li, Cheng Chen:
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization. - Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner:
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. - Junbiao Cui, Jiye Liang:
Fuzzy Learning Machine. - Shuchen Wu, Noémi Élteto, Ishita Dasgupta, Eric Schulz:
Learning Structure from the Ground up - Hierarchical Representation Learning by Chunking. - Yuanfeng Ji, Haotian Bai, Chongjian Ge, Jie Yang, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, Ping Luo:
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation. - Jean Barbier, TianQi Hou, Marco Mondelli, Manuel Sáenz:
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? - Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan:
Scalable Interpretability via Polynomials. - Jiawei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, Mike Zheng Shou:
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes. - Haoyu Chen, Linqi Song, Zhenxing Qian, Xinpeng Zhang, Kede Ma:
Hiding Images in Deep Probabilistic Models. - Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu:
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints. - Le Hui, Linghua Tang, Yaqi Shen, Jin Xie, Jian Yang:
Learning Superpoint Graph Cut for 3D Instance Segmentation. - Song Liu:
Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC. - Marie Maros, Gesualdo Scutari:
Acceleration in Distributed Sparse Regression. - Yizeng Han, Zhihang Yuan, Yifan Pu, Chenhao Xue, Shiji Song, Guangyu Sun, Gao Huang:
Latency-aware Spatial-wise Dynamic Networks. - Hanqing Wang, Wei Liang, Luc Van Gool, Wenguan Wang:
Towards Versatile Embodied Navigation. - Artyom Y. Sorokin, Nazar Buzun, Leonid Pugachev, Mikhail Burtsev:
Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes. - Yen-Cheng Liu, Chih-Yao Ma, Junjiao Tian, Zijian He, Zsolt Kira:
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks. - Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Jianhao Wang, Alex Yuan Gao, Wenzhe Li, Liang Bin, Chelsea Finn, Chongjie Zhang:
LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning. - Abhishek Aich, Calvin-Khang Ta, Akash Gupta, Chengyu Song, Srikanth V. Krishnamurthy, M. Salman Asif, Amit Roy-Chowdhury:
GAMA: Generative Adversarial Multi-Object Scene Attacks. - Yunan Lu, Xiuyi Jia:
Predicting Label Distribution from Multi-label Ranking. - Kiwon Lee, Andrew N. Cheng, Elliot Paquette, Courtney Paquette:
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions. - Yongyi Yang, Zengfeng Huang, David P. Wipf:
Transformers from an Optimization Perspective. - Jiayuan Mao, Tomás Lozano-Pérez, Josh Tenenbaum, Leslie Pack Kaelbling:
PDSketch: Integrated Domain Programming, Learning, and Planning. - Khai Nguyen, Nhat Ho:
Amortized Projection Optimization for Sliced Wasserstein Generative Models. - Jinsung Jeon, Jeonghak Kim, Haryong Song, Seunghyeon Cho, Noseong Park:
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks. - Yuntao Liu, Yuan Li, Xinhai Xu, Yong Dou, Donghong Liu:
Heterogeneous Skill Learning for Multi-agent Tasks. - Gal Vardi, Ohad Shamir, Nati Srebro:
On Margin Maximization in Linear and ReLU Networks. - Titas Anciukevicius, Patrick Fox-Roberts, Edward Rosten, Paul Henderson:
Unsupervised Causal Generative Understanding of Images. - Daiheng Gao, Yuliang Xiu, Kailin Li, Lixin Yang, Feng Wang, Peng Zhang, Bang Zhang, Cewu Lu, Ping Tan:
DART: Articulated Hand Model with Diverse Accessories and Rich Textures. - Xiangrui Cai, Haidong Xu, Sihan Xu, Ying Zhang, Xiaojie Yuan:
BadPrompt: Backdoor Attacks on Continuous Prompts. - Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. - Xin Du, Kumiko Tanaka-Ishii:
FIRE: Semantic Field of Words Represented as Non-Linear Functions. - Ruqi Zhang, Qiang Liu, Xin T. Tong:
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. - Carlos Misael Madrid Padilla, Daren Wang, Zifeng Zhao, Yi Yu:
Change-point Detection for Sparse and Dense Functional Data in General Dimensions. - Ting-An Chen, De-Nian Yang, Ming-Syan Chen:
ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences. - Eslam Mohamed Bakr, Yasmeen Alsaedy, Mohamed Elhoseiny:
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding. - Luofeng Liao, Yuan Gao, Christian Kroer:
Nonstationary Dual Averaging and Online Fair Allocation. - Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu:
Incentivizing Combinatorial Bandit Exploration. - Ioana Bica, Mihaela van der Schaar:
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation. - Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? - Alberto Silvio Chiappa, Alessandro Marin Vargas, Alexander Mathis:
DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body. - Valentin Thomas:
On the role of overparameterization in off-policy Temporal Difference learning with linear function approximation. - Felix Draxler, Christoph Schnörr, Ullrich Köthe:
Whitening Convergence Rate of Coupling-based Normalizing Flows. - Halley Young, Maxwell Du, Osbert Bastani:
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints. - Dennis Fassmeyer, Pascal Fassmeyer, Ulf Brefeld:
Semi-Supervised Generative Models for Multiagent Trajectories. - Yasmin Salehi, Dennis Giannacopoulos:
PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery. - Yabo Zhang, Mingshuai Yao, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, Wangmeng Zuo:
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks. - Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D. Manning, Percy Liang, Jure Leskovec:
Deep Bidirectional Language-Knowledge Graph Pretraining. - Mingliang Ma, Abolfazl Safikhani:
Theoretical analysis of deep neural networks for temporally dependent observations. - Nick Alonso, Beren Millidge, Jeffrey L. Krichmar, Emre O. Neftci:
A Theoretical Framework for Inference Learning. - Emanuele Aiello, Diego Valsesia, Enrico Magli:
Cross-modal Learning for Image-Guided Point Cloud Shape Completion. - Sagi Lotan, Ernesto Evgeniy Sanches Shayda, Dan Feldman:
Coreset for Line-Sets Clustering. - Rujie Zhong, Duohan Zhang, Lukas Schäfer, Stefano V. Albrecht, Josiah Hanna:
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning. - Yao-Xiang Ding, Xi-Zhu Wu, Kun Zhou, Zhi-Hua Zhou:
Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning. - Alexander Thebelt, Calvin Tsay, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Behrang Shafei, Ruth Misener:
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces. - Hangjie Yuan, Jianwen Jiang, Samuel Albanie, Tao Feng, Ziyuan Huang, Dong Ni, Mingqian Tang:
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection. - Minjong Yoo, Sangwoo Cho, Honguk Woo:
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning. - Yiyun Luo, Will Wei Sun, Yufeng Liu:
Contextual Dynamic Pricing with Unknown Noise: Explore-then-UCB Strategy and Improved Regrets. - Omar Montasser, Steve Hanneke, Nati Srebro:
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. - Nathan Kallus, James McInerney:
The Implicit Delta Method. - Yanpeng Sun, Qiang Chen, Xiangyu He, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jian Cheng, Zechao Li, Jingdong Wang:
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning. - Louis Annabi, Alexandre Pitti, Mathias Quoy:
On the relationship between variational inference and auto-associative memory. - Yossi Arjevani, Michael Field:
Annihilation of Spurious Minima in Two-Layer ReLU Networks. - Shentong Mo, Pedro Morgado:
A Closer Look at Weakly-Supervised Audio-Visual Source Localization. - Zihao Wang, Ziyin Liu:
Posterior Collapse of a Linear Latent Variable Model. - Jialun Zhang, Hong-Ming Chiu, Richard Y. Zhang:
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion. - Harit Vishwakarma, Frederic Sala:
Lifting Weak Supervision To Structured Prediction. - Juan Elenter, Navid Naderializadeh, Alejandro Ribeiro:
A Lagrangian Duality Approach to Active Learning. - Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. - Fernando Paolo, Tsu-ting Tim Lin, Ritwik Gupta, Bryce Goodman, Nirav Patel, Daniel Kuster, David Kroodsma, Jared Dunnmon:
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery. - Jiaxi Wang, Ji Wu, Lei Huang:
Understanding the Failure of Batch Normalization for Transformers in NLP. - Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel:
Exploration via Elliptical Episodic Bonuses. - Xiaozhuang Song, Shun Zheng, Wei Cao, James J. Q. Yu, Jiang Bian:
Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations. - Anh Tong, Thanh Nguyen-Tang, Toan M. Tran, Jaesik Choi:
Learning Fractional White Noises in Neural Stochastic Differential Equations. - Ran Ran, Wei Wang, Quan Gang, Jieming Yin, Nuo Xu, Wujie Wen:
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference. - Ziqi Pan, Li Niu, Liqing Zhang:
UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator. - Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao:
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup. - Hao Sun, Lei Han, Rui Yang, Xiaoteng Ma, Jian Guo, Bolei Zhou:
Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping. - Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Stability and Generalization for Markov Chain Stochastic Gradient Methods. - Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan, Henghui Ding, Yulun Zhang, Radu Timofte, Luc Van Gool:
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging. - Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine, Stefanos Nikolaidis:
Deep Surrogate Assisted Generation of Environments. - Taiki Miyagawa:
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis. - Congzheng Song, Filip Granqvist, Kunal Talwar:
FLAIR: Federated Learning Annotated Image Repository. - Zaiwei Zhang, Min Bai, Li Erran Li:
Self-Supervised Pretraining for Large-Scale Point Clouds. - Samy Jelassi, Michael E. Sander, Yuanzhi Li:
Vision Transformers provably learn spatial structure. - Boyang Deng, Sumith Kulal, Zhengyang Dong, Congyue Deng, Yonglong Tian, Jiajun Wu:
Unsupervised Learning of Shape Programs with Repeatable Implicit Parts. - Wanshan Li, Alessandro Rinaldo, Daren Wang:
Detecting Abrupt Changes in Sequential Pairwise Comparison Data. - Chuofan Ma, Qiushan Guo, Yi Jiang, Ping Luo, Zehuan Yuan, Xiaojuan Qi:
Rethinking Resolution in the Context of Efficient Video Recognition. - Randall Balestriero, Léon Bottou, Yann LeCun:
The Effects of Regularization and Data Augmentation are Class Dependent. - Gianluca Brero, Eric Mibuari, Nicolas Lepore, David C. Parkes:
Learning to Mitigate AI Collusion on Economic Platforms. - Simina Brânzei, Noam Nisan:
The Query Complexity of Cake Cutting. - Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani:
PAC Prediction Sets for Meta-Learning. - Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang:
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. - Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite:
Pruning's Effect on Generalization Through the Lens of Training and Regularization. - Jennifer Brennan, Vahab Mirrokni, Jean Pouget-Abadie:
Cluster Randomized Designs for One-Sided Bipartite Experiments. - Ashwini Pokle, Zhengyang Geng, J. Zico Kolter:
Deep Equilibrium Approaches to Diffusion Models. - Yibo Yang, Shixiang Chen, Xiangtai Li, Liang Xie, Zhouchen Lin, Dacheng Tao:
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network? - Murad Tukan, Loay Mualem, Alaa Maalouf:
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions. - Yuanwei Liu, Nian Liu, Xiwen Yao, Junwei Han:
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation. - Yuchong Sun, Hongwei Xue, Ruihua Song, Bei Liu, Huan Yang, Jianlong Fu:
Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning. - Frédéric Piedboeuf, Philippe Langlais:
A new dataset for multilingual keyphrase generation. - Jerry Chee, Megan Flynn, Anil Damle, Christopher De Sa:
Model Preserving Compression for Neural Networks. - Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen:
Neural Conservation Laws: A Divergence-Free Perspective. - Yang Zhao, Chen Zhang, Haifeng Huang, Haoyuan Li, Zhou Zhao:
Towards Effective Multi-Modal Interchanges in Zero-Resource Sounding Object Localization. - Shiji Zhou, Wenpeng Zhang, Jiyan Jiang, Wenliang Zhong, Jinjie Gu, Wenwu Zhu:
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond. - Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov:
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. - Konstantin Schürholt, Diyar Taskiran, Boris Knyazev, Xavier Giró-i-Nieto, Damian Borth:
Model Zoos: A Dataset of Diverse Populations of Neural Network Models. - Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng, Thomas H. Li, Mingkui Tan, Chuang Gan:
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation. - Federica Zoe Ricci, Michele Guindani, Erik B. Sudderth:
Thinned random measures for sparse graphs with overlapping communities. - Michiel A. Bakker, Martin J. Chadwick, Hannah Sheahan, Michael Henry Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt M. Botvinick, Christopher Summerfield:
Fine-tuning language models to find agreement among humans with diverse preferences. - Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang:
What is a Good Metric to Study Generalization of Minimax Learners? - Yuki Tatsunami, Masato Taki:
Sequencer: Deep LSTM for Image Classification. - Jiafei Lyu, Xiu Li, Zongqing Lu:
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination. - Tommaso Salvatori, Luca Pinchetti, Beren Millidge, Yuhang Song, Tianyi Bao, Rafal Bogacz, Thomas Lukasiewicz:
Learning on Arbitrary Graph Topologies via Predictive Coding. - Samuel Dooley, George Z. Wei, Tom Goldstein, John Dickerson:
Robustness Disparities in Face Detection. - Khoa D. Doan, Yingjie Lao, Ping Li:
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class. - Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, Armen Aghajanyan:
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models. - Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang:
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. - Zhiqi Bu, Jialin Mao, Shiyun Xu:
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. - Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S. Cohen:
Weakly supervised causal representation learning. - Hualin Zhang, Huan Xiong, Bin Gu:
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients. - Peng Jiang, Lihan Hu, Shihui Song:
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training. - Alex Tamkin, Gaurab Banerjee, Mohamed Owda, Vincent Liu, Shashank Rammoorthy, Noah D. Goodman:
DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision. - Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Operator Splitting Value Iteration. - Chao Ren, Yizhong Pan, Jie Huang:
Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization. - Hengyuan Hu, Samuel Sokota, David J. Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob N. Foerster:
Self-Explaining Deviations for Coordination. - Chuanhao Li, Hongning Wang:
Communication Efficient Federated Learning for Generalized Linear Bandits. - Ying-Peng Tang, Sheng-Jun Huang:
Active Learning for Multiple Target Models. - Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David P. Wipf:
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. - Qiang Li, Chung-Yiu Yau, Hoi-To Wai:
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents. - Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, Se-Young Yun:
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning. - Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar:
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. - Benjamin Coleman, Santiago Segarra, Alexander J. Smola, Anshumali Shrivastava:
Graph Reordering for Cache-Efficient Near Neighbor Search. - Jiangmeng Li, Wenwen Qiang, Yanan Zhang, Wenyi Mo, Changwen Zheng, Bing Su, Hui Xiong:
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning. - Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson:
On Feature Learning in the Presence of Spurious Correlations. - Tianyu Wang, Xiaowei Hu, Zhengzhe Liu, Chi-Wing Fu:
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection. - Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur:
Exploring Length Generalization in Large Language Models. - Yunwen Lei, Rong Jin, Yiming Ying:
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks. - Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao:
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation. - Oren Mangoubi, Nisheeth K. Vishnoi:
Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion. - Mengda Xu, Manuela Veloso, Shuran Song:
ASPiRe: Adaptive Skill Priors for Reinforcement Learning. - Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. - Marvin Zhang, Sergey Levine, Chelsea Finn:
MEMO: Test Time Robustness via Adaptation and Augmentation. - Elena Grigorescu, Young-San Lin, Sandeep Silwal, Maoyuan Song, Samson Zhou:
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming. - Chengzhi Lin, Ancong Wu, Junwei Liang, Jun Zhang, Wenhang Ge, Wei-Shi Zheng, Chunhua Shen:
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval. - Huiyang Shao, Qianqian Xu, Zhiyong Yang, Shilong Bao, Qingming Huang:
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm. - Sho Sonoda, Isao Ishikawa, Masahiro Ikeda:
Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups. - Yoni Choukroun, Lior Wolf:
Error Correction Code Transformer. - Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, Mohsen Ali:
Towards Improving Calibration in Object Detection Under Domain Shift. - Jiachen T. Wang, Saeed Mahloujifar, Shouda Wang, Ruoxi Jia, Prateek Mittal:
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning. - Zhishan Li, Ying Nie, Kai Han, Jianyuan Guo, Lei Xie, Yunhe Wang:
A Transformer-Based Object Detector with Coarse-Fine Crossing Representations. - Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Michalák, Shahab Asoodeh, Flávio P. Calmon:
Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection. - Mazda Moayeri, Kiarash Banihashem, Soheil Feizi:
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness. - Zhiyuan Wang, Xovee Xu, Weifeng Zhang, Goce Trajcevski, Ting Zhong, Fan Zhou:
Learning Latent Seasonal-Trend Representations for Time Series Forecasting. - Archit Bansal, Danny Stoll, Maciej Janowski, Arber Zela, Frank Hutter:
JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search. - Csaba Tóth, Darrick Lee, Celia Hacker, Harald Oberhauser:
Capturing Graphs with Hypo-Elliptic Diffusions. - Duc Nguyen, Anderson Ye Zhang:
A Spectral Approach to Item Response Theory. - A. Tuan Nguyen, Philip H. S. Torr, Ser Nam Lim:
FedSR: A Simple and Effective Domain Generalization Method for Federated Learning. - Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman:
SIXO: Smoothing Inference with Twisted Objectives. - Ze Gong, Yu Zhang:
Explicable Policy Search. - Mingyang Hu, Fajie Yuan, Kevin Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding:
Exploring evolution-aware & -free protein language models as protein function predictors. - Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, Alec Jacobson:
Breaking Bad: A Dataset for Geometric Fracture and Reassembly. - Jacobus G. M. van der Linden, Mathijs de Weerdt, Emir Demirovic:
Fair and Optimal Decision Trees: A Dynamic Programming Approach. - Chao Feng, Wuchao Li, Defu Lian, Zheng Liu, Enhong Chen:
Recommender Forest for Efficient Retrieval. - Song Wang, Chen Chen, Jundong Li:
Graph Few-shot Learning with Task-specific Structures. - Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar:
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning. - Quentin Bertrand, Quentin Klopfenstein, Pierre-Antoine Bannier, Gauthier Gidel, Mathurin Massias:
Beyond L1: Faster and Better Sparse Models with skglm. - Keiran Paster, Sheila A. McIlraith, Jimmy Ba:
You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments. - Linjian Ma, Edgar Solomonik:
Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs. - Chengxuan Zhu, Renjie Wan, Boxin Shi:
Neural Transmitted Radiance Fields. - Joy Hsu, Jiajun Wu, Noah D. Goodman:
Geoclidean: Few-Shot Generalization in Euclidean Geometry. - Jihoon Chung, Yu Wu, Olga Russakovsky:
Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation. - Zheyuan Jiang, Jingyue Gao, Jianyu Chen:
Unsupervised Skill Discovery via Recurrent Skill Training. - Matthias Bitzer, Mona Meister, Christoph Zimmer:
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport. - Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper:
Robust Models are less Over-Confident. - Gabriele Farina, Ioannis Anagnostides, Haipeng Luo, Chung-Wei Lee, Christian Kroer, Tuomas Sandholm:
Near-Optimal No-Regret Learning Dynamics for General Convex Games. - Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang:
OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport. - Hyun Lee, Taehyun Kim, Hyolim Kang, Minjoo Ki, Hyeonchan Hwang, Kwanho Park, Sharang Han, Seon Joo Kim:
ComMU: Dataset for Combinatorial Music Generation.
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