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ORCID26th AISTATS 2023: Valencia, Spain
- Francisco J. R. Ruiz, Jennifer G. Dy, Jan-Willem van de Meent:
International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain. Proceedings of Machine Learning Research 206, PMLR 2023 - Shinsaku Sakaue, Taihei Oki:
Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation. 1-10 - Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner:
Meta-Uncertainty in Bayesian Model Comparison. 11-29 - Jie Shen:
PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time. 30-46 - Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh:
Entropic Risk Optimization in Discounted MDPs. 47-76 - Elias Samuel Wirth, Thomas Kerdreux, Sebastian Pokutta:
Acceleration of Frank-Wolfe Algorithms with Open-Loop Step-Sizes. 77-100 - Lianke Qin, Zhao Song, Lichen Zhang, Danyang Zhuo:
An Online and Unified Algorithm for Projection Matrix Vector Multiplication with Application to Empirical Risk Minimization. 101-156 - Jieyu Zhang, Linxin Song, Alex Ratner:
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision. 157-171 - Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou:
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. 172-235 - Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas:
Scalable marked point processes for exchangeable and non-exchangeable event sequences. 236-252 - Martin Jankowiak:
Bayesian Variable Selection in a Million Dimensions. 253-282 - Yulai Zhao, Jianshu Chen, Simon S. Du:
Blessing of Class Diversity in Pre-training. 283-305 - Rayyan Ahmad Khan, Martin Kleinsteuber:
Barlow Graph Auto-Encoder for Unsupervised Network Embedding. 306-322 - Karim Tit, Teddy Furon, Mathias Rousset:
Gradient-Informed Neural Network Statistical Robustness Estimation. 323-334 - Andi Nika, Adish Singla, Goran Radanovic:
Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning. 335-358 - Vivien Cabannes, Stefano Vigogna:
A Case of Exponential Convergence Rates for SVM. 359-374 - Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang:
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. 375-407 - Simon Bartels, Kristoffer Stensbo-Smidt
, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg:
Adaptive Cholesky Gaussian Processes. 408-452 - Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili:
Sample Complexity of Kernel-Based Q-Learning. 453-469 - Hadrien Hendrikx:
A principled framework for the design and analysis of token algorithms. 470-489 - Mohsen Heidari, Wojciech Szpankowski:
Learning k-qubit Quantum Operators via Pauli Decomposition. 490-504 - Shiwei Zeng, Jie Shen:
Semi-Verified PAC Learning from the Crowd. 505-522 - Michal Sharoni, Sivan Sabato:
On the Capacity Limits of Privileged ERM. 523-534 - Kyungsu Lee, Haeyun Lee, Jae Youn Hwang:
USIM Gate: UpSampling Module for Segmenting Precise Boundaries concerning Entropy. 535-562 - Yang Yang, Gennaro Gala, Robert Peharz:
Bayesian Structure Scores for Probabilistic Circuits. 563-575 - Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. 576-593 - Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan:
Overcoming Prior Misspecification in Online Learning to Rank. 594-614 - Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik:
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. 615-649 - Kei Ishikawa, Niao He:
Kernel Conditional Moment Constraints for Confounding Robust Inference. 650-674 - Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Yasuhiro Fujiwara:
Meta-learning for Robust Anomaly Detection. 675-691 - Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri:
Learning in RKHM: a C*-Algebraic Twist for Kernel Machines. 692-708 - Sebastian Bordt, Ulrike von Luxburg:
From Shapley Values to Generalized Additive Models and back. 709-745 - Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel:
Estimating Conditional Average Treatment Effects with Missing Treatment Information. 746-766 - Zenan Ling, Xingyu Xie, Qiuhao Wang, Zongpeng Zhang, Zhouchen Lin:
Global Convergence of Over-parameterized Deep Equilibrium Models. 767-787 - Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava:
A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space. 788-836 - Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton:
Adversarial De-confounding in Individualised Treatment Effects Estimation. 837-849 - Tom Hess, Ron Visbord, Sivan Sabato:
Fast Distributed k-Means with a Small Number of Rounds. 850-874 - Xinwei Sun, Xiangyu Zheng, Jim Weinstein:
A New Causal Decomposition Paradigm towards Health Equity. 875-890 - Arshak Minasyan, Tigran Galstyan, Sona Hunanyan, Arnak S. Dalalyan:
Matching Map Recovery with an Unknown Number of Outliers. 891-906 - Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong:
Characterizing Internal Evasion Attacks in Federated Learning. 907-921 - Duc Nguyen, Anderson Ye Zhang:
Optimal and Private Learning from Human Response Data. 922-958 - Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson:
Bayesian Optimization with Conformal Prediction Sets. 959-986 - Quan Xiao, Han Shen, Wotao Yin, Tianyi Chen:
Alternating Projected SGD for Equality-constrained Bilevel Optimization. 987-1023 - Sivan Sabato:
Improved Robust Algorithms for Learning with Discriminative Feature Feedback. 1024-1036 - Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis:
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations. 1037-1054 - Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Can 5th Generation Local Training Methods Support Client Sampling? Yes! 1055-1092 - Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier:
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. 1093-1114 - Natraj Raman, Daniele Magazzeni, Sameena Shah:
Bayesian Hierarchical Models for Counterfactual Estimation. 1115-1128 - Xianyang Zhang, Trisha Dawn:
Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis. 1129-1143 - Runzhe Wan, Lin Ge, Rui Song:
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework. 1144-1173 - Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey:
Compress Then Test: Powerful Kernel Testing in Near-linear Time. 1174-1218 - Hanni Cheng, Haosi Zheng, Ya Cong, Weihao Jiang, Shiliang Pu:
Select and Optimize: Learning to aolve large-scale TSP instances. 1219-1231 - Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. 1232-1300 - Ansuman Banerjee, Shayak Chakraborty, Sourav Chakraborty, Kuldeep S. Meel, Uddalok Sarkar, Sayantan Sen:
Testing of Horn Samplers. 1301-1330 - Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh, Xi Liu:
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees. 1331-1378 - Konstantin Klemmer, Nathan S. Safir, Daniel B. Neill:
Positional Encoder Graph Neural Networks for Geographic Data. 1379-1389 - Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. 1390-1410 - Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause:
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. 1411-1436 - Hengchao Chen, Xiang Li, Qiang Sun:
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices. 1437-1456 - Saeyoung Rho, Rachel Cummings, Vishal Misra:
Differentially Private Synthetic Control. 1457-1491 - Felix Jimenez, Matthias Katzfuss:
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. 1492-1512 - Hongru Yang, Zhangyang Wang:
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks. 1513-1553 - Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Riemannian Accelerated Gradient Methods via Extrapolation. 1554-1585 - Matthew J. Holland:
Flexible risk design using bi-directional dispersion. 1586-1623 - Jung-Hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes:
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles. 1624-1645 - Russell Tsuchida, Cheng Soon Ong:
Deep equilibrium models as estimators for continuous latent variables. 1646-1671 - Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Erick Lavoie, Anne-Marie Kermarrec:
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data. 1672-1702 - Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. 1703-1718 - Joachim Spoerhase, Kamyar Khodamoradi, Benedikt Riegel, Bruno Ordozgoiti, Aristides Gionis:
A Constant-Factor Approximation Algorithm for Reconciliation k-Median. 1719-1746 - Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar:
Neural Laplace Control for Continuous-time Delayed Systems. 1747-1778 - Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner:
Discovering Many Diverse Solutions with Bayesian Optimization. 1779-1798 - Vitalii Bulygin, Dmytro Mykheievskyi, Kyrylo Kuchynskyi:
BlitzMask: Real-Time Instance Segmentation Approach for Mobile Devices. 1799-1811 - Jane H. Lee, Saeid Haghighatshoar, Amin Karbasi:
Exact Gradient Computation for Spiking Neural Networks via Forward Propagation. 1812-1831 - Mingshan Sun, Ye Zheng, Tianpeng Bao, Jianqiu Chen, Guoqiang Jin, Liwei Wu, Rui Zhao, Xiaoke Jiang:
Uni6Dv2: Noise Elimination for 6D Pose Estimation. 1832-1844 - Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol:
Multilevel Bayesian Quadrature. 1845-1868 - Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Madalina Fiterau:
Direct Inference of Effect of Treatment (DIET) for a Cookieless World. 1869-1887 - Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell, Aaron Schein:
The Ordered Matrix Dirichlet for State-Space Models. 1888-1903 - Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew Duncan:
Energy-Based Models for Functional Data using Path Measure Tilting. 1904-1923 - Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer:
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. 1924-1941 - Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart:
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. 1942-1964 - Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar:
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning. 1965-2001 - Chengkuan Hong, Christian R. Shelton:
Variational Inference for Neyman-Scott Processes. 2002-2018 - Giannis Nikolentzos, Michalis Vazirgiannis:
Graph Alignment Kernels using Weisfeiler and Leman Hierarchies. 2019-2034 - Giannis Nikolentzos, Michalis Vazirgiannis:
Geometric Random Walk Graph Neural Networks via Implicit Layers. 2035-2053 - Shalev Shaer, Gal Maman, Yaniv Romano:
Model-X Sequential Testing for Conditional Independence via Testing by Betting. 2054-2086 - Imad Aouali, Branislav Kveton, Sumeet Katariya:
Mixed-Effect Thompson Sampling. 2087-2115 - Nelvin Tan, Ramji Venkataramanan:
Mixed Linear Regression via Approximate Message Passing. 2116-2131 - Yirui Liu, Xinghao Qiao, Liying Wang, Jessica Lam:
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model. 2132-2146 - Ke Bai, Pengyu Cheng, Weituo Hao, Ricardo Henao, Larry Carin:
Estimating Total Correlation with Mutual Information Estimators. 2147-2164 - Çagin Ararat, Cem Tekin:
Vector Optimization with Stochastic Bandit Feedback. 2165-2190 - Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake:
Knowledge Acquisition for Human-In-The-Loop Image Captioning. 2191-2206 - Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan:
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. 2207-2261 - Ziqing Xu, Hancheng Min, Salma Tarmoun, Enrique Mallada, René Vidal:
Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization. 2262-2284 - Jerry Chee, Hwanwoo Kim, Panos Toulis:
"Plus/minus the learning rate": Easy and Scalable Statistical Inference with SGD. 2285-2309 - Rick Presman, Jason Xu:
Distance-to-Set Priors and Constrained Bayesian Inference. 2310-2326 - Achal Awasthi, Jason Xu:
Fast Computation of Branching Process Transition Probabilities via ADMM. 2327-2347 - Junwen Yao, N. Benjamin Erichson, Miles E. Lopes:
Error Estimation for Random Fourier Features. 2348-2364 - Feihu Huang, Xidong Wu, Zhengmian Hu:
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization. 2365-2389 - Jaromír Plhák, Ondrej Sotolár, Michaela Lebedíková, David Smahel:
Classification of Adolescents' Risky Behavior in Instant Messaging Conversations. 2390-2404 - Tom Norman, Nir Weinberger, Kfir Y. Levy:
Robust Linear Regression for General Feature Distribution. 2405-2435 - Solenne Gaucher, Nicolas Schreuder, Evgenii Chzhen:
Fair learning with Wasserstein barycenters for non-decomposable performance measures. 2436-2459 - Matthias Rath, Alexandru Paul Condurache:
Deep Neural Networks with Efficient Guaranteed Invariances. 2460-2480 - Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai:
Fast Block Coordinate Descent for Non-Convex Group Regularizations. 2481-2493 - Andrea Pugnana, Salvatore Ruggieri:
AUC-based Selective Classification. 2494-2514 - Shashank Singh:
Nonparametric Indirect Active Learning. 2515-2541 - Loay Mualem, Moran Feldman:
Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets. 2542-2564 - Jixiang Qing, Henry B. Moss, Tom Dhaene, Ivo Couckuyt:
PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization. 2565-2588 - Hedda Cohen Indelman, Tamir Hazan:
Learning Constrained Structured Spaces with Application to Multi-Graph Matching. 2589-2602 - El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang:
On the Strategyproofness of the Geometric Median. 2603-2640 - Young-Geun Kim, Ying Liu, Xuexin Wei:
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE. 2641-2660 - Lev Telyatnikov, Simone Scardapane:
EGG-GAE: scalable graph neural networks for tabular data imputation. 2661-2676 - Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao:
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables. 2677-2703 - Xun Zhu, Yutong Xiong, Ming Wu, Gaozhen Nie, Bin Zhang, Ziheng Yang:
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations. 2704-2722 - Yiling Luo, Yiling Xie, Xiaoming Huo:
Improved Rate of First Order Algorithms for Entropic Optimal Transport. 2723-2750 - Yingying Zhang, Chengchun Shi, Shikai Luo:
Conformal Off-Policy Prediction. 2751-2768 - Jeremy Sellier, Petros Dellaportas:
Sparse Spectral Bayesian Permanental Process with Generalized Kernel. 2769-2791 - Huishuai Zhang, Da Yu, Yiping Lu, Di He:
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. 2792-2804 - Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun:
Nearly Optimal Latent State Decoding in Block MDPs. 2805-2904 - Quentin Bertrand, Wojciech Marian Czarnecki, Gauthier Gidel:
On the Limitations of the Elo, Real-World Games are Transitive, not Additive. 2905-2921 - Mohsen Heidari, Wojciech Szpankowski:
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression. 2922-2938 - Zhenbang Wang, Emanuel Ben-David, Martin Slawski:
Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group. 2939-2959 - Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee:
Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. 2960-2974 - Daniel Goldfarb, Paul Hand:
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime. 2975-2993 - Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. 2994-3017 - Hussein Hazimeh, Natalia Ponomareva:
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets. 3018-3033 - Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu:
Learning Physics-Informed Neural Networks without Stacked Back-propagation. 3034-3047 - Kihyuk Hong, Yuhang Li, Ambuj Tewari:
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge. 3048-3085 - David Simchi-Levi, Chonghuan Wang:
Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference. 3086-3097 - Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh:
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits. 3098-3124 - Ziye Ma, Somayeh Sojoudi:
Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate. 3125-3150 - Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan:
Byzantine-Robust Federated Learning with Optimal Statistical Rates. 3151-3178 - Yinglong Guo, Dongmian Zou, Gilad Lerman:
An Unpooling Layer for Graph Generation. 3179-3209 - Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, Marco Pavone:
Online Learning for Traffic Routing under Unknown Preferences. 3210-3229 - Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. 3230-3269 - Mengxiao Zhang, Shi Chen, Haipeng Luo, Yingfei Wang:
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution. 3270-3298 - Aidan Scannell, Carl Henrik Ek, Arthur Richards:
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes. 3299-3314 - Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu:
Generative Oversampling for Imbalanced Data via Majority-Guided VAE. 3315-3330 - Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan:
The Lie-Group Bayesian Learning Rule. 3331-3352 - Dan Meller, Nicolas Berkouk:
Singular Value Representation: A New Graph Perspective On Neural Networks. 3353-3369 - Shengbo Wang, Nian Si, José H. Blanchet, Zhengyuan Zhou:
A Finite Sample Complexity Bound for Distributionally Robust Q-learning. 3370-3398 - Hiroshi Morioka, Aapo Hyvärinen:
Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. 3399-3426 - Oisin Faust, Hamza Fawzi, James Saunderson:
A Bregman Divergence View on the Difference-of-Convex Algorithm. 3427-3439 - Arnab Kumar Mondal, Lakshya Singhal, Piyush Tiwary, Parag Singla, Prathosh AP:
Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders. 3440-3465 - Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. 3466-3492 - Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. 3493-3514 - Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu, Yu Yang:
Online Learning for Non-monotone DR-Submodular Maximization: From Full Information to Bandit Feedback. 3515-3537 - Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman:
Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection. 3538-3567 - Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar:
To Impute or not to Impute? Missing Data in Treatment Effect Estimation. 3568-3590 - Sebastian Shenghong Tay, Quoc Phong Nguyen, Chuan Sheng Foo, Bryan Kian Hsiang Low:
No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities. 3591-3619 - Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela:
Noise-Aware Statistical Inference with Differentially Private Synthetic Data. 3620-3643 - Louis Leconte, Sholom Schechtman, Eric Moulines:
ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks. 3644-3663 - Alberto Cabezas, Christopher Nemeth:
Transport Elliptical Slice Sampling. 3664-3676 - Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Zhiri Yuan, Qi Wu, Siyi Wang, Dongdong Wang, Zhixiang Huang:
Towards Balanced Representation Learning for Credit Policy Evaluation. 3677-3692 - Lukang Sun, Avetik G. Karagulyan, Peter Richtárik:
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. 3693-3717 - Maxim Kodryan, Dmitry Kropotov, Dmitry P. Vetrov:
MARS: Masked Automatic Ranks Selection in Tensor Decompositions. 3718-3732 - Song Duong, Alberto Lumbreras, Mike Gartrell, Patrick Gallinari:
Learning from Multiple Sources for Data-to-Text and Text-to-Data. 3733-3753 - Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy:
Sparse Bayesian optimization. 3754-3774 - Anass Aghbalou, Anne Sabourin, François Portier:
On the bias of K-fold cross validation with stable learners. 3775-3794 - Yohan Jung, Jinkyoo Park:
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior. 3795-3824 - Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei:
Sample Efficiency of Data Augmentation Consistency Regularization. 3825-3853 - Thomas Kleine Buening, Aadirupa Saha:
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. 3854-3878 - Changwoo Lee, Xiao Hu, Hun-Seok Kim:
Deep Joint Source-Channel Coding with Iterative Source Error Correction. 3879-3902 - Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang, Xutong Liu, Mohammad H. Hajiesmaili, John C. S. Lui, Don Towsley:
On-Demand Communication for Asynchronous Multi-Agent Bandits. 3903-3930 - Simon Damm, Dennis Forster, Dmytro Velychko, Zhenwen Dai, Asja Fischer, Jörg Lücke:
The ELBO of Variational Autoencoders Converges to a Sum of Entropies. 3931-3960 - Joshua C. Chang, Carson C. Chow, Julia Porcino:
Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery. 3961-3976 - Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang:
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. 3977-4005 - Hanlin Zhu, Ruosong Wang, Jason D. Lee:
Provably Efficient Reinforcement Learning via Surprise Bound. 4006-4032 - Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, Bryan Kian Hsiang Low:
FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. 4033-4057 - Austin J. Stromme:
Sampling From a Schrödinger Bridge. 4058-4067 - Yehu Chen, Annamaria Prati, Jacob M. Montgomery, Roman Garnett:
A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data. 4068-4088 - Naoya Takeishi, Alexandros Kalousis:
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models. 4089-4100 - Aarshvi Gajjar, Christopher Musco, Chinmay Hegde:
Active Learning for Single Neuron Models with Lipschitz Non-Linearities. 4101-4113 - Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi:
Exploration in Reward Machines with Low Regret. 4114-4146 - Alankrita Bhatt, J. Jon Ryu, Young-Han Kim:
On Universal Portfolios with Continuous Side Information. 4147-4163 - Yaxuan Zhu, Jianwen Xie, Ping Li:
Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation. 4164-4180 - Ilya Shpitser:
The Lauritzen-Chen Likelihood For Graphical Models. 4181-4195 - Emmanouil Zampetakis, Fred Zhang:
Bayesian Strategy-Proof Facility Location via Robust Estimation. 4196-4208 - William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani:
Unsupervised representation learning with recognition-parametrised probabilistic models. 4209-4230 - Samuel Allen Alexander, David Quarel, Len Du, Marcus Hutter:
Universal Agent Mixtures and the Geometry of Intelligence. 4231-4246 - Wei-Ning Chen, Ayfer Özgür, Graham Cormode, Akash Bharadwaj:
The communication cost of security and privacy in federated frequency estimation. 4247-4274 - Zixian Yang, R. Srikant, Lei Ying:
Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB. 4275-4312 - Sujay Bhatt, Guanhua Fang, Ping Li:
Piecewise Stationary Bandits under Risk Criteria. 4313-4335 - Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo:
Does Label Differential Privacy Prevent Label Inference Attacks? 4336-4347 - Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang:
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. 4348-4380 - Hiroki Naganuma, Hideaki Iiduka:
Conjugate Gradient Method for Generative Adversarial Networks. 4381-4408 - Davin Choo, Kirankumar Shiragur:
Subset verification and search algorithms for causal DAGs. 4409-4442 - Han Shen, Songtao Lu, Xiaodong Cui, Tianyi Chen:
Distributed Offline Policy Optimization Over Batch Data. 4443-4472 - Rui Wang, Pengyu Cheng, Ricardo Henao:
Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling. 4473-4485 - Christian Fabian, Kai Cui, Heinz Koeppl:
Learning Sparse Graphon Mean Field Games. 4486-4514 - Joel Oskarsson, Per Sidén, Fredrik Lindsten:
Temporal Graph Neural Networks for Irregular Data. 4515-4531 - Xingyu Xu, Yuantao Gu:
Oblivious near-optimal sampling for multidimensional signals with Fourier constraints. 4532-4555 - Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause:
Isotropic Gaussian Processes on Finite Spaces of Graphs. 4556-4574 - Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
A Tale of Sampling and Estimation in Discounted Reinforcement Learning. 4575-4601 - Shaokui Wei, Jiayin Liu, Bing Li, Hongyuan Zha:
Mean Parity Fair Regression in RKHS. 4602-4628 - Xiangqian Sun, Cheuk Hang Leung, Yijun Li, Qi Wu:
A Unified Perspective on Regularization and Perturbation in Differentiable Subset Selection. 4629-4642 - Ryuichiro Hataya, Makoto Yamada:
Nyström Method for Accurate and Scalable Implicit Differentiation. 4643-4654 - Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen:
Fair Representation Learning with Unreliable Labels. 4655-4667 - Pavan Karjol, Rohan Kashyap, Prathosh AP:
Neural Discovery of Permutation Subgroups. 4668-4678 - Duy Nguyen, Ngoc Bui, Viet Anh Nguyen:
Feasible Recourse Plan via Diverse Interpolation. 4679-4698 - Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski:
Computing Abductive Explanations for Boosted Trees. 4699-4711 - Kevin Elgui, Alex Nowak, Geneviève Robin:
A Statistical Learning Take on the Concordance Index for Survival Analysis. 4712-4731 - Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic:
An Efficient and Continuous Voronoi Density Estimator. 4732-4744 - Giovanni Luca Marchetti, Gustaf Tegnér, Anastasiia Varava, Danica Kragic:
Equivariant Representation Learning via Class-Pose Decomposition. 4745-4756 - Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins:
Approximating a RUM from Distributions on k-Slates. 4757-4767 - Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger:
Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. 4768-4801 - Johan Larsson, Quentin Klopfenstein, Mathurin Massias, Jonas Wallin:
Coordinate Descent for SLOPE. 4802-4821 - Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil:
Multi-task Representation Learning with Stochastic Linear Bandits. 4822-4847 - Gianluigi Lopardo, Frédéric Precioso, Damien Garreau:
A Sea of Words: An In-Depth Analysis of Anchors for Text Data. 4848-4879 - Rosanne Turner, Peter Grunwald:
Safe Sequential Testing and Effect Estimation in Stratified Count Data. 4880-4893 - Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. 4894-4916 - Hossein Taheri, Christos Thrampoulidis:
On Generalization of Decentralized Learning with Separable Data. 4917-4945 - Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan:
Neural Simulated Annealing. 4946-4962 - Yusha Liu, Aarti Singh:
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits. 4963-4985 - François Bachoc, Louis Béthune, Alberto González-Sanz, Jean-Michel Loubes:
Gaussian Processes on Distributions based on Regularized Optimal Transport. 4986-5010 - Yuxuan Han, Jialin Zeng, Yang Wang, Yang Xiang, Jiheng Zhang:
Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles. 5011-5035 - Lukasz Struski, Marcin Mazur, Pawel Batorski, Przemyslaw Spurek, Jacek Tabor:
Bounding Evidence and Estimating Log-Likelihood in VAE. 5036-5051 - Lijie Hu, Zihang Xiang, Jiabin Liu, Di Wang:
Privacy-preserving Sparse Generalized Eigenvalue Problem. 5052-5062 - Minh-Toan Nguyen, Romain Couillet:
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture. 5063-5078 - Muhammad Faaiz Taufiq, Patrick Blöbaum, Lenon Minorics:
Manifold Restricted Interventional Shapley Values. 5079-5106 - Yutong Dai, Guanyi Wang, Frank E. Curtis, Daniel P. Robinson:
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization. 5107-5133 - Juan Kuntz, Jen Ning Lim, Adam M. Johansen:
Particle algorithms for maximum likelihood training of latent variable models. 5134-5180 - Jonas Wacker, Ruben Ohana, Maurizio Filippone:
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel. 5181-5212 - Henry B. Moss, Sebastian W. Ober, Victor Picheny:
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation. 5213-5230 - Quan Nguyen, Roman Garnett:
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery. 5231-5249 - Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar:
Efficient fair PCA for fair representation learning. 5250-5270 - Masahiro Kato, Masaaki Imaizumi, Kentaro Minami:
Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics. 5271-5298 - Vincent Plassier, Eric Moulines, Alain Durmus:
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. 5299-5356 - David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright:
Adversarial Random Forests for Density Estimation and Generative Modeling. 5357-5375 - Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar:
Smoothly Giving up: Robustness for Simple Models. 5376-5410 - Xiaoyan Hu, Ho-fung Leung:
A Tighter Problem-Dependent Regret Bound for Risk-Sensitive Reinforcement Learning. 5411-5437 - Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup:
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. 5438-5448 - Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy:
Implications of sparsity and high triangle density for graph representation learning. 5449-5473 - Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White:
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. 5474-5492 - Ludovic Arnould, Claire Boyer, Erwan Scornet:
Is interpolation benign for random forest regression? 5493-5548 - Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag:
TabLLM: Few-shot Classification of Tabular Data with Large Language Models. 5549-5581 - Simon Barthelmé, Nicolas Tremblay, Pierre-Olivier Amblard:
A Faster Sampler for Discrete Determinantal Point Processes. 5582-5592 - Andrew Stirn, Harm Wessels, Megan Schertzer, Laura Pereira, Neville E. Sanjana, David A. Knowles:
Faithful Heteroscedastic Regression with Neural Networks. 5593-5613 - Ben London, Levi Lu, Ted Sandler, Thorsten Joachims:
Boosted Off-Policy Learning. 5614-5640 - Sarath Pattathil, Kaiqing Zhang, Asuman E. Ozdaglar:
Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence. 5641-5685 - Nikita Puchkin, Valeriia Shcherbakova:
A Contrastive Approach to Online Change Point Detection. 5686-5713 - Truc D. T. Nguyen, Phung Lai, Khang Tran, NhatHai Phan, My T. Thai:
Active Membership Inference Attack under Local Differential Privacy in Federated Learning. 5714-5730 - Lingxiao Wang, Boxin Zhao, Mladen Kolar:
Differentially Private Matrix Completion through Low-rank Matrix Factorization. 5731-5748 - Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn:
Private Non-Convex Federated Learning Without a Trusted Server. 5749-5786 - Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov:
Variational Boosted Soft Trees. 5787-5801 - Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi, Andreas Krause:
The Schrödinger Bridge between Gaussian Measures has a Closed Form. 5802-5833 - Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip B. Gibbons:
Federated Learning under Distributed Concept Drift. 5834-5853 - Zachary Robertson, Hantao Zhang, Sanmi Koyejo:
Cooperative Inverse Decision Theory for Uncertain Preferences. 5854-5868 - Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag:
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. 5869-5898 - Yuqing Hu, Stéphane Pateux, Vincent Gripon:
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification. 5899-5917 - Dmitry M. Malioutov, Sanjeeb Dash, Dennis Wei:
Heavy Sets with Applications to Interpretable Machine Learning Diagnostics. 5918-5930 - Sasila Ilandarideva, Yannis Bekri, Anatoli Iouditski, Vianney Perchet:
Stochastic Mirror Descent for Large-Scale Sparse Recovery. 5931-5957 - Evan Becker, Jingdong Gao, Ted Zadouri, Baharan Mirzasoleiman:
High Probability Bounds for Stochastic Continuous Submodular Maximization. 5958-5979 - Sung Jae Jun, Sokbae Lee:
Average Adjusted Association: Efficient Estimation with High Dimensional Confounders. 5980-5996 - Jiachen Yang, Tarik Dzanic, Brenden K. Petersen, Jun Kudo, Ketan Mittal, Vladimir Z. Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio V. Kolev, Robert W. Anderson, Daniel M. Faissol:
Reinforcement Learning for Adaptive Mesh Refinement. 5997-6014 - Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka:
Risk Bounds on Aleatoric Uncertainty Recovery. 6015-6036 - Nathan Kallus, Miruna Oprescu:
Robust and Agnostic Learning of Conditional Distributional Treatment Effects. 6037-6060 - Benjamin Howson, Ciara Pike-Burke, Sarah Filippi:
Optimism and Delays in Episodic Reinforcement Learning. 6061-6094 - Benjamin Howson, Ciara Pike-Burke, Sarah Filippi:
Delayed Feedback in Generalised Linear Bandits Revisited. 6095-6119 - Nikolay Krantsevich, Jingyu He, P. Richard Hahn:
Stochastic Tree Ensembles for Estimating Heterogeneous Effects. 6120-6131 - Rong Tang, Yun Yang:
Minimax Nonparametric Two-Sample Test under Adversarial Losses. 6132-6165 - Helmuth J. Naumer, Farzad Kamalabadi:
Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon Forecasting. 6166-6198 - Tamara A. Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri H. Souza:
Distill n' Explain: explaining graph neural networks using simple surrogates. 6199-6214 - Yueyang Liu, Benjamin Van Roy, Kuang Xu:
Nonstationary Bandit Learning via Predictive Sampling. 6215-6244 - Zhiyue Zhang, Hongyuan Mei, Yanxun Xu:
Continuous-Time Decision Transformer for Healthcare Applications. 6245-6262 - Aadirupa Saha, Aldo Pacchiano, Jonathan Lee:
Dueling RL: Reinforcement Learning with Trajectory Preferences. 6263-6289 - Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans:
Discrete Langevin Samplers via Wasserstein Gradient Flow. 6290-6313 - Haotian Ju, Dongyue Li, Aneesh Sharma, Hongyang R. Zhang:
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion. 6314-6341 - Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári:
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. 6342-6370 - Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter:
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. 6371-6387 - Jiachen T. Wang, Ruoxi Jia:
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning. 6388-6421 - Cyrus Cousins:
Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare. 6422-6442 - Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Animashree Anandkumar:
Distributionally Robust Policy Gradient for Offline Contextual Bandits. 6443-6462 - Hideaki Ishibashi, Masayuki Karasuyama, Ichiro Takeuchi, Hideitsu Hino:
A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets. 6463-6497 - Qihan Wang, Chen Dun, Fangshuo Liao, Chris Jermaine, Anastasios Kyrillidis:
LOFT: Finding Lottery Tickets through Filter-wise Training. 6498-6526 - Honghao Wei, Arnob Ghosh, Ness B. Shroff, Lei Ying, Xingyu Zhou:
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs. 6527-6570 - Fasil Cheema, Ruth Urner:
Precision Recall Cover: A Method For Assessing Generative Models. 6571-6594 - Zihan Wang, Jason Lee, Qi Lei:
Reconstructing Training Data from Model Gradient, Provably. 6595-6612 - Ji Wang, Ding Lu, Ian Davidson, Zhaojun Bai:
Scalable Spectral Clustering with Group Fairness Constraints. 6613-6629 - Chen Dun, Mirian Hipolito Garcia, Chris Jermaine, Dimitrios Dimitriadis, Anastasios Kyrillidis:
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout. 6630-6660 - Toshihiro Ota, Ikuro Sato, Rei Kawakami, Masayuki Tanaka, Nakamasa Inoue:
Learning with Partial Forgetting in Modern Hopfield Networks. 6661-6673 - Weilin Cong, Mehrdad Mahdavi:
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection. 6674-6703 - Yunzhe Zhou, Zhengling Qi, Chengchun Shi, Lexin Li:
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach. 6704-6721 - Yucheng Wang, Mingyuan Zhou, Yu Sun, Xiaoning Qian:
Uncertainty-aware Unsupervised Video Hashing. 6722-6740 - Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen:
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. 6741-6763 - Christopher Harker, Aditya Bhaskara:
Structure of Nonlinear Node Embeddings in Stochastic Block Models. 6764-6782 - Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima:
On Model Selection Consistency of Lasso for High-Dimensional Ising Models. 6783-6805 - Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition. 6806-6821 - Ning Liu, Yue Yu, Huaiqian You, Neeraj Tatikola:
INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation. 6822-6838 - Laurence Davies, Robert Salomone, Matthew Sutton, Chris Drovandi:
Transport Reversible Jump Proposals. 6839-6852 - Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark W. Barrett, Eitan Farchi:
Convex Bounds on the Softmax Function with Applications to Robustness Verification. 6853-6878 - Zheyang Xiong, Fangshuo Liao, Anastasios Kyrillidis:
Strong Lottery Ticket Hypothesis with ε-perturbation. 6879-6902 - Sayak Chatterjee, Dibyendu Saha, Soham Dan, Bhaswar B. Bhattacharya:
Two-Sample Tests for Inhomogeneous Random Graphs in Lr Norm: Optimality and Asymptotics. 6903-6911 - Quanhan Xi, Benjamin Bloem-Reddy:
Indeterminacy in Generative Models: Characterization and Strong Identifiability. 6912-6939 - Paramita Koley, Harshavardhan Alimi, Shrey Singla, Sourangshu Bhattacharya, Niloy Ganguly, Abir De:
Differentiable Change-point Detection With Temporal Point Processes. 6940-6955 - Adrian N. Bishop, Edwin V. Bonilla:
Recurrent Neural Networks and Universal Approximation of Bayesian Filters. 6956-6967 - Nishant A. Mehta, Junpei Komiyama, Vamsi K. Potluru, Andrea Nguyen, Mica Grant-Hagen:
Thresholded linear bandits. 6968-7020 - Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson:
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. 7021-7039 - Munir Hiabu, Joseph T. Meyer, Marvin N. Wright:
Unifying local and global model explanations by functional decomposition of low dimensional structures. 7040-7060 - Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, Vikash K. Mansinghka:
SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. 7061-7088 - Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová:
On double-descent in uncertainty quantification in overparametrized models. 7089-7125 - Sofia Triantafillou, Fattaneh Jabbari, Gregory F. Cooper:
Learning Treatment Effects from Observational and Experimental Data. 7126-7146 - Zhiqiang Xu:
On the Accelerated Noise-Tolerant Power Method. 7147-7175 - Chandramauli Chakraborty, Sayan Paul, Saptarshi Chakraborty, Swagatam Das:
Clustering High-dimensional Data with Ordered Weighted ℓ1 Regularization. 7176-7189 - Aldo Gael Carranza, Sanath Kumar Krishnamurthy, Susan Athey:
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles. 7190-7212 - Dan Garber, Tsur Livney, Shoham Sabach:
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. 7213-7238 - Shibo Li, Zheng Wang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Meta-Learning with Adjoint Methods. 7239-7251 - Ankur Ankan, Inge M. N. Wortel, Kenneth Bollen, Johannes Textor:
Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models. 7252-7264 - Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach:
Explicit Regularization in Overparametrized Models via Noise Injection. 7265-7287 - Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, Ruqi Zhang:
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation. 7288-7310 - Théo Lacombe:
An Homogeneous Unbalanced Regularized Optimal Transport Model with Applications to Optimal Transport with Boundary. 7311-7330 - Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. 7331-7348 - Mojmir Mutny, Tadeusz Janik, Andreas Krause:
Active Exploration via Experiment Design in Markov Chains. 7349-7374 - Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan:
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. 7375-7391 - Victor Boone, Bruno Gaujal:
Identification of Blackwell Optimal Policies for Deterministic MDPs. 7392-7424 - Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski:
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources. 7425-7454 - Fares Fourati, Vaneet Aggarwal, Christopher J. Quinn, Mohamed-Slim Alouini:
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback. 7455-7471 - Michelle Chen, Olga Ohrimenko:
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms. 7472-7491 - Xiaolu Wang, Yuchen Jiao, Hoi-To Wai, Yuantao Gu:
Incremental Aggregated Riemannian Gradient Method for Distributed PCA. 7492-7510 - Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson, Divya Grover, Emilio Jorge:
Minimax-Bayes Reinforcement Learning. 7511-7527 - Natasa Tagasovska, Firat Ozdemir, Axel Brando:
Retrospective Uncertainties for Deep Models using Vine Copulas. 7528-7539 - Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. 7540-7577 - Hang Zhang, Ping Li:
Improved Bound on Generalization Error of Compressed KNN Estimator. 7578-7593 - Shuoguang Yang, Yuhao Yan, Xiuneng Zhu, Qiang Sun:
Online Linearized LASSO. 7594-7610 - Prashant Trivedi, Nandyala Hemachandra:
Multi-Agent congestion cost minimization with linear function approximations. 7611-7643 - Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Q. Phung:
Global-Local Regularization Via Distributional Robustness. 7644-7664 - Daesoo Lee, Sara Malacarne, Erlend Aune:
Vector Quantized Time Series Generation with a Bidirectional Prior Model. 7665-7693 - Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth:
Do Bayesian Neural Networks Need To Be Fully Stochastic? 7694-7722 - Patrick Saux, Odalric Maillard:
Risk-aware linear bandits with convex loss. 7723-7754 - Pierre Gaillard, Aadirupa Saha, Soham Dan:
One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. 7755-7773 - Samuel Stocksieker, Denys Pommeret, Arthur Charpentier:
Data Augmentation for Imbalanced Regression. 7774-7799 - Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney:
Fast Feature Selection with Fairness Constraints. 7800-7823 - Qin-Cheng Zheng, Shen-Huan Lyu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou:
On the Consistency Rate of Decision Tree Learning Algorithms. 7824-7848 - Grigor Keropyan, David Strieder, Mathias Drton:
Rank-Based Causal Discovery for Post-Nonlinear Models. 7849-7870 - Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric:
On the Complexity of Representation Learning in Contextual Linear Bandits. 7871-7896 - Matthias Bitzer, Mona Meister, Christoph Zimmer:
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems. 7897-7912 - Jing Wang, Peng Zhao, Zhi-Hua Zhou:
Revisiting Weighted Strategy for Non-stationary Parametric Bandits. 7913-7942 - Rob Romijnders, Yuki M. Asano, Christos Louizos, Max Welling:
No time to waste: practical statistical contact tracing with few low-bit messages. 7943-7960 - Alicia Curth, Mihaela van der Schaar:
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. 7961-7980 - Jia-Wei Shan, Peng Zhao, Zhi-Hua Zhou:
Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions. 7981-7998 - Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki:
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games. 7999-8028 - Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Model-Based Uncertainty in Value Functions. 8029-8052 - Itay Evron, Ophir Onn, Tamar Weiss Orzech, Hai Azeroual, Daniel Soudry:
The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study. 8053-8077 - Marina Drygala, Sai Ganesh Nagarajan, Ola Svensson:
Online Algorithms with Costly Predictions. 8078-8101 - Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc, Charles Margossian, Sam Power:
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories. 8102-8116 - Taira Tsuchiya, Shinji Ito, Junya Honda:
Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits. 8117-8144 - Michael Sucker, Peter Ochs:
PAC-Bayesian Learning of Optimization Algorithms. 8145-8164 - Felix Biggs, Benjamin Guedj:
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty. 8165-8182 - Tyler Maunu, Thibaut Le Gouic, Philippe Rigollet:
Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery. 8183-8210 - Ralph E. Q. Urlus, Max Baak, Stéphane Collot, Ilan Fridman Rojas:
Pointwise sampling uncertainties on the Precision-Recall curve. 8211-8232 - Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan:
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference. 8233-8262 - Osman Mian, David Kaltenpoth, Michael Kamp, Jilles Vreeken:
Nothing but Regrets - Privacy-Preserving Federated Causal Discovery. 8263-8278 - Thomas M. McDonald, Magnus Ross, Michael T. Smith, Mauricio A. Álvarez:
Nonparametric Gaussian Process Covariances via Multidimensional Convolutions. 8279-8293 - Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar:
Improved Representation Learning Through Tensorized Autoencoders. 8294-8307 - Frederiek Wesel, Kim Batselier:
Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data. 8308-8320 - Cheuk Ting Li, Farzan Farnia:
Mode-Seeking Divergences: Theory and Applications to GANs. 8321-8350 - Yu Wang, Mikolaj Kasprzak, Jonathan H. Huggins:
A Targeted Accuracy Diagnostic for Variational Approximations. 8351-8372 - Zhe Huang, Mary-Joy Sidhom, Benjamin Wessler, Michael C. Hughes:
Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data. 8373-8394 - Charles K. Assaad, Imad Ez-zejjari, Lei Zan:
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops. 8395-8404 - Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh:
Principled Approaches for Private Adaptation from a Public Source. 8405-8432 - Aman Shrivastava, Ramprasaath R. Selvaraju, Nikhil Naik, Vicente Ordonez:
CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision. 8433-8447 - Dongping Qi, David Bindel, Alexander Vladimirsky:
Surveillance Evasion Through Bayesian Reinforcement Learning. 8448-8462 - Zhichao Wang, Yizhe Zhu:
Overparameterized Random Feature Regression with Nearly Orthogonal Data. 8463-8493 - Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel R. D. Rodrigues, Vincent Y. F. Tan:
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? 8494-8520 - Tam Le, Truyen Nguyen, Kenji Fukumizu:
Scalable Unbalanced Sobolev Transport for Measures on a Graph. 8521-8560 - Jayadev Acharya, Yuhan Liu, Ziteng Sun:
Discrete Distribution Estimation under User-level Local Differential Privacy. 8561-8585 - Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas:
Causal Entropy Optimization. 8586-8605 - Seungjae Shin, HeeSun Bae, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon:
Loss-Curvature Matching for Dataset Selection and Condensation. 8606-8628 - Kyriakos Lotidis, Nicholas Bambos, Jose H. Blanchet, Jiajin Li:
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints. 8629-8644 - Josh Givens, Song Liu, Henry W. J. Reeve:
Density Ratio Estimation and Neyman Pearson Classification with Missing Data. 8645-8681 - James Thornton, Marco Cuturi:
Rethinking Initialization of the Sinkhorn Algorithm. 8682-8698 - Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb:
Representation Learning in Deep RL via Discrete Information Bottleneck. 8699-8722 - Abdullah Alchihabi, Yuhong Guo:
Learning Robust Graph Neural Networks with Limited Supervision. 8723-8733 - Shuqi Liu, Yuzhou Cao, Qiaozhen Zhang, Lei Feng, Bo An:
Consistent Complementary-Label Learning via Order-Preserving Losses. 8734-8748 - Tom Huix, Matthew Zhang, Alain Durmus:
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. 8749-8770 - Lukas Zierahn, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Gergely Neu:
Nonstochastic Contextual Combinatorial Bandits. 8771-8813 - Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei:
Probabilistic Conformal Prediction Using Conditional Random Samples. 8814-8836 - Srshti Putcha, Christopher Nemeth, Paul Fearnhead:
Preferential Subsampling for Stochastic Gradient Langevin Dynamics. 8837-8856 - Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On the Calibration of Probabilistic Classifier Sets. 8857-8870 - Manuele Leonelli, Gherardo Varando:
Context-Specific Causal Discovery for Categorical Data Using Staged Trees. 8871-8888 - Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal:
Federated Learning for Data Streams. 8889-8924 - Neil Jethani, Adriel Saporta, Rajesh Ranganath:
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. 8925-8953 - Asif Khan, Amos J. Storkey:
Adversarial robustness of VAEs through the lens of local geometry. 8954-8967 - Haotian Ye, James Zou, Linjun Zhang:
Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise. 8968-8990 - Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai:
Learning to Optimize with Stochastic Dominance Constraints. 8991-9009 - Yuhang He, Andrew Markham:
SoundSynp: Sound Source Detection from Raw Waveforms with Multi-Scale Synperiodic Filterbanks. 9010-9023 - Dheeraj Baby, Yu-Xiang Wang:
Second Order Path Variationals in Non-Stationary Online Learning. 9024-9075 - Syrine Belakaria, Janardhan Rao Doppa, Nicolò Fusi, Rishit Sheth:
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach. 9076-9093 - Thomas Gebhart, Jakob Hansen, Paul Schrater:
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding. 9094-9116 - Ting Cai, Kirthevasan Kandasamy:
Active Cost-aware Labeling of Streaming Data. 9117-9136 - Eric Xia, Martin J. Wainwright:
Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces. 9137-9166 - Minyoung Kim:
SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval. 9167-9190 - Achraf Bahamou, Donald Goldfarb, Yi Ren:
A Mini-Block Fisher Method for Deep Neural Networks. 9191-9220 - Elvis Dohmatob, Chuan Guo, Morgane Goibert:
Origins of Low-Dimensional Adversarial Perturbations. 9221-9237 - Hongchang Gao, Bin Gu, My T. Thai:
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network. 9238-9281 - Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Pricing against a Budget and ROI Constrained Buyer. 9282-9307 - Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew McCallum:
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining. 9308-9330 - Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang:
Incentive-aware Contextual Pricing with Non-parametric Market Noise. 9331-9361 - Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang, Michael Bohlke-Schneider:
Coherent Probabilistic Forecasting of Temporal Hierarchies. 9362-9376 - Nicolas Christianson, Junxuan Shen, Adam Wierman:
Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems. 9377-9399 - Cyril Bachelard, Apostolos Chalkis, Vissarion Fisikopoulos, Elias P. Tsigaridas:
Randomized geometric tools for anomaly detection in stock markets. 9400-9416 - Brian Liu, Rahul Mazumder:
ForestPrune: Compact Depth-Pruned Tree Ensembles. 9417-9428 - Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. 9429-9469 - Christoph Dann, Mohammad Ghavamzadeh, Teodor V. Marinov:
Multiple-policy High-confidence Policy Evaluation. 9470-9487 - Benjie Wang, Marta Kwiatkowska:
Compositional Probabilistic and Causal Inference using Tractable Circuit Models. 9488-9498 - Sara Ahmadian, Maryam Negahbani:
Improved Approximation for Fair Correlation Clustering. 9499-9516 - Victoria G. Crawford:
Scalable Bicriteria Algorithms for Non-Monotone Submodular Cover. 9517-9537 - Gavin Kerrigan, Justin Ley, Padhraic Smyth:
Diffusion Generative Models in Infinite Dimensions. 9538-9563 - Namrata Deka, Danica J. Sutherland:
MMD-B-Fair: Learning Fair Representations with Statistical Testing. 9564-9576 - Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Missingness Shift. 9577-9606 - Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang:
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. 9607-9636 - Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai:
Adapting to Latent Subgroup Shifts via Concepts and Proxies. 9637-9661 - Hongjian Wang, Aaditya Ramdas:
Huber-robust confidence sequences. 9662-9679 - Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel:
On the Privacy Risks of Algorithmic Recourse. 9680-9696 - Zhaoyue Chen, Yifan Sun:
Reducing Discretization Error in the Frank-Wolfe Method. 9697-9727 - Zaiyan Xu, Kishan Panaganti, Dileep Kalathil:
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning. 9728-9754 - Daniel P. Jeong, Seyoung Kim:
Factorial SDE for Multi-Output Gaussian Process Regression. 9755-9772 - Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran:
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets. 9773-9806 - Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, Dacheng Tao, Yingbin Liang, Zhangyang Wang:
Learning to Generalize Provably in Learning to Optimize. 9807-9825 - Pranjal Awasthi, Corinna Cortes, Christopher Mohri:
Theory and Algorithm for Batch Distribution Drift Problems. 9826-9851 - Anna Winnicki, R. Srikant:
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation. 9852-9878 - Hongwei Shang, Jean-Marc Langlois, Kostas Tsioutsiouliklis, Changsung Kang:
Precision/Recall on Imbalanced Test Data. 9879-9891 - Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf:
Iterative Teaching by Data Hallucination. 9892-9913 - Dan Qiao, Yu-Xiang Wang:
Near-Optimal Differentially Private Reinforcement Learning. 9914-9940 - Jianyu Xu, Dan Qiao, Yu-Xiang Wang:
Doubly Fair Dynamic Pricing. 9941-9975 - Garud Iyengar, Henry Lam, Tianyu Wang:
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation. 9976-10011 - Ang Li, Judea Pearl:
Probabilities of Causation: Role of Observational Data. 10012-10027 - Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui:
Influence Diagnostics under Self-concordance. 10028-10076 - Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. 10077-10094 - Dragana Bajovic, Dusan Jakovetic, Soummya Kar:
Large deviations rates for stochastic gradient descent with strongly convex functions. 10095-10111 - Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaïd Harchaoui:
Stochastic Optimization for Spectral Risk Measures. 10112-10159 - Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. 10160-10177 - Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt, Tamer Basar:
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path. 10178-10206 - Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo de los Campos, Ian Fischer:
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. 10207-10222 - Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz:
Ideal Abstractions for Decision-Focused Learning. 10223-10234 - Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Probabilistic Querying of Continuous-Time Event Sequences. 10235-10251 - Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
Semantic Strengthening of Neuro-Symbolic Learning. 10252-10261 - Caleb Dahlke, Sue Zheng, Jason Pacheco:
Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models. 10262-10278 - Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liò, Mihaela van der Schaar:
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. 10279-10304 - Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani:
A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem. 10305-10323 - Yao Yao, Qihang Lin, Tianbao Yang:
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. 10324-10342 - Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath:
DIET: Conditional independence testing with marginal dependence measures of residual information. 10343-10367 - Qi Chen, Mario Marchand:
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. 10368-10394 - Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth:
Actually Sparse Variational Gaussian Processes. 10395-10408 - Aditya Bhaskara, Kamesh Munagala:
Competing against Adaptive Strategies in Online Learning via Hints. 10409-10424 - Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous:
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. 10425-10444 - Spencer Compton, Dmitriy Katz, Benjamin Qi, Kristjan H. Greenewald, Murat Kocaoglu:
Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier. 10445-10469 - Kaiqi Zhao, Animesh Jain, Ming Zhao:
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions. 10470-10486 - Jayadev Acharya, Sourbh Bhadane, Arnab Bhattacharyya, Saravanan Kandasamy, Ziteng Sun:
Sample Complexity of Distinguishing Cause from Effect. 10487-10504 - Shay Deutsch, Stefano Soatto:
Graph Spectral Embedding using the Geodesic Betweenness Centrality. 10505-10519 - Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag:
Who Should Predict? Exact Algorithms For Learning to Defer to Humans. 10520-10545 - Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Score-based Quickest Change Detection for Unnormalized Models. 10546-10565 - Xingyu Lu, Hasin Us Sami, Basak Güler:
Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity. 10566-10593 - Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David C. Parkes:
Reinforcement Learning with Stepwise Fairness Constraints. 10594-10618 - Xinyue Xia, Gal Mishne, Yusu Wang:
Implicit Graphon Neural Representation. 10619-10634 - Ga Ming Angus Chan, Tianxi Li:
Fitting low-rank models on egocentrically sampled partial networks. 10635-10649 - Gary Cheng, Karan N. Chadha, John C. Duchi:
Federated Asymptotics: a model to compare federated learning algorithms. 10650-10689 - Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag:
Conformalized Unconditional Quantile Regression. 10690-10702 - Chengliang Tang, Nathan Lenssen, Ying Wei, Tian Zheng:
Wasserstein Distributional Learning via Majorization-Minimization. 10703-10731 - Ziang Chen, Jianfeng Lu, Huajie Qian, Xinshang Wang, Wotao Yin:
HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent. 10732-10781 - Joseph Janssen, Vincent Guan, Elina Robeva:
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees. 10782-10814 - Tina Behnia, Ganesh Ramachandra Kini, Vala Vakilian, Christos Thrampoulidis:
On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data. 10815-10838 - Ekaterina Khramtsova, Guido Zuccon, Xi Wang, Mahsa Baktashmotlagh:
Convolutional Persistence as a Remedy to Neural Model Analysis. 10839-10855 - Wanqiao Xu, Yecheng Jason Ma, Kan Xu, Hamsa Bastani, Osbert Bastani:
Uniformly Conservative Exploration in Reinforcement Learning. 10856-10870 - Andrew Bennett, Dipendra Misra, Nathan Kallus:
Provable Safe Reinforcement Learning with Binary Feedback. 10871-10900 - Adam N. Elmachtoub, Vishal Gupta, Yunfan Zhao:
Balanced Off-Policy Evaluation for Personalized Pricing. 10901-10917 - Kurtland Chua, Qi Lei, Jason D. Lee:
Provable Hierarchy-Based Meta-Reinforcement Learning. 10918-10967 - Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, Richard G. Baraniuk:
A Blessing of Dimensionality in Membership Inference through Regularization. 10968-10993 - Koji Tabata, Junpei Komiyama, Atsuyoshi Nakamura, Tamiki Komatsuzaki:
Posterior Tracking Algorithm for Classification Bandits. 10994-11022 - Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka:
The Power of Recursion in Graph Neural Networks for Counting Substructures. 11023-11042 - Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck:
Mixtures of All Trees. 11043-11058 - Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn, Zico Kolter:
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes. 11059-11078 - Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel:
Performative Prediction with Neural Networks. 11079-11093 - Xingyu Xu, Yuantao Gu:
Benign overfitting of non-smooth neural networks beyond lazy training. 11094-11117 - Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi:
A New Modeling Framework for Continuous, Sequential Domains. 11118-11131 - Jackie Baek, Vivek F. Farias:
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation. 11132-11148 - Kush Bhatia, Wenshuo Guo, Jacob Steinhardt:
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws. 11149-11171 - Roy Dong, Heling Zhang, Lillian J. Ratliff:
Approximate Regions of Attraction in Learning with Decision-Dependent Distributions. 11172-11184 - Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh, Necdet S. Aybat:
Randomized Primal-Dual Methods with Adaptive Step Sizes. 11185-11212 - Amur Ghose, Yingxue Zhang, Jianye Hao, Mark Coates:
Spectral Augmentations for Graph Contrastive Learning. 11213-11266 - Hyunglip Bae, Jinkyu Lee, Woo Chang Kim, Yongjae Lee:
Deep Value Function Networks for Large-Scale Multistage Stochastic Programs. 11267-11287 - Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. 11288-11316 - Koki Okajima, Xiangming Meng, Takashi Takahashi, Yoshiyuki Kabashima:
Average case analysis of Lasso under ultra sparse conditions. 11317-11330 - Sebastian Gruber, Florian Buettner:
Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition. 11331-11354 - Hiromi Narimatsu, Mayuko Ozawa, Shiro Kumano:
Collision Probability Matching Loss for Disentangling Epistemic Uncertainty from Aleatoric Uncertainty. 11355-11370 - David Bosch, Ashkan Panahi, Ayça Özçelikkale, Devdatt P. Dubhashi:
Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves. 11371-11414 - Rajeev Verma, Daniel Barrejón, Eric T. Nalisnick:
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. 11415-11434 - Mingtian Zhang, Yitong Sun, Chen Zhang, Steven McDonagh:
Spread Flows for Manifold Modelling. 11435-11456 - Grzegorz Gluch, Khashayar Barooti, Rüdiger L. Urbanke:
Breaking a Classical Barrier for Classifying Arbitrary Test Examples in the Quantum Model. 11457-11488 - Nikolaos Nakis, Abdulkadir Çelikkanat, Louis Boucherie, Christian Djurhuus, Felix Burmester, Daniel Mathias Holmelund, Monika Frolcová, Morten Mørup:
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model. 11489-11505 - Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander V. Gasnikov, Martin Takác:
Algorithm for Constrained Markov Decision Process with Linear Convergence. 11506-11533 - Xuantong Liu, Jianfeng Zhang, Tianyang Hu, He Cao, Yuan Yao, Lujia Pan:
Inducing Neural Collapse in Deep Long-tailed Learning. 11534-11544 - Arber Qoku, Florian Buettner:
Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity. 11545-11562 - Lawrence Stewart, Francis R. Bach, Quentin Berthet, Jean-Philippe Vert:
Regression as Classification: Influence of Task Formulation on Neural Network Features. 11563-11582 - Meyer Scetbon, Elvis Dohmatob:
Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond. 11583-11607 - Shelvia Wongso, Rohan Ghosh, Mehul Motani:
Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks. 11608-11629 - Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski:
Probing Graph Representations. 11630-11649 - Christoph Luther, Gunnar König, Moritz Grosse-Wentrup:
Efficient SAGE Estimation via Causal Structure Learning. 11650-11670
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