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ORCIDJournal of Machine Learning Research, Volume 25
Volume 25, 2024
- Jonathan K. Su:
On Truthing Issues in Supervised Classification. 1:1-1:91 - Yuze Han, Guangzeng Xie, Zhihua Zhang:
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction. 2:1-2:86 - Zheng Tracy Ke, Jun S. Liu, Yucong Ma:
Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic. 3:1-3:67 - Shicong Cen, Yuting Wei, Yuejie Chi:
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization. 4:1-4:48 - Ernesto Araya Valdivia, Guillaume Braun, Hemant Tyagi:
Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method. 5:1-5:43 - Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
Model-Free Representation Learning and Exploration in Low-Rank MDPs. 6:1-6:76 - Isabella Verdinelli, Larry A. Wasserman:
Decorrelated Variable Importance. 7:1-7:27 - Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh:
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models. 8:1-8:46 - Zixian Yang, Xin Liu, Lei Ying:
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment. 9:1-9:55 - Daniel Cirkovic, Tiandong Wang:
Modeling Random Networks with Heterogeneous Reciprocity. 10:1-10:40 - Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov:
Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design. 11:1-11:37 - Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn:
Critically Assessing the State of the Art in Neural Network Verification. 12:1-12:53 - Stefan Ankirchner, Stefan Perko:
A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent. 13:1-13:55 - Alex Bihlo:
Improving physics-informed neural networks with meta-learned optimization. 14:1-14:26 - Sebastian Neumayer, Lénaïc Chizat, Michael Unser:
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks. 15:1-15:24 - Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. 16:1-16:59 - Bing Li, Kyongwon Kim:
On Sufficient Graphical Models. 17:1-17:64 - Ryan Giordano, Martin Ingram, Tamara Broderick:
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. 18:1-18:39 - Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang:
Nonparametric Inference under B-bits Quantization. 19:1-19:68 - Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts:
Iterate Averaging in the Quest for Best Test Error. 20:1-20:55 - Shotaro Yagishita, Jun-ya Gotoh:
Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension. 21:1-21:42 - Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang:
On the Generalization of Stochastic Gradient Descent with Momentum. 22:1-22:56 - Xiaowu Dai, Lexin Li:
Post-Regularization Confidence Bands for Ordinary Differential Equations. 23:1-23:51 - Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. 24:1-24:67 - Etrit Haxholli, Marco Lorenzi:
On Tail Decay Rate Estimation of Loss Function Distributions. 25:1-25:47 - Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. 26:1-26:36 - Stephan Wojtowytsch:
Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality. 27:1-27:49 - Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff:
Additive smoothing error in backward variational inference for general state-space models. 28:1-28:33 - Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov:
Rates of convergence for density estimation with generative adversarial networks. 29:1-29:47 - Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi:
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent. 30:1-30:27 - Dahuin Jung, Hyungyu Lee, Sungroh Yoon:
Sample-efficient Adversarial Imitation Learning. 31:1-31:32 - Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang:
Heterogeneous-Agent Reinforcement Learning. 32:1-32:67 - Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan:
Pygmtools: A Python Graph Matching Toolkit. 33:1-33:7 - Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan A. Murphy, Jonas Peters:
Effect-Invariant Mechanisms for Policy Generalization. 34:1-34:36 - Shijun Zhang, Jianfeng Lu, Hongkai Zhao:
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions. 35:1-35:39 - Kayhan Behdin, Rahul Mazumder:
Sparse NMF with Archetypal Regularization: Computational and Robustness Properties. 36:1-36:62 - T. Tony Cai, Hongji Wei:
Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms. 37:1-37:63 - Rina Foygel Barber, Emil Y. Sidky:
Convergence for nonconvex ADMM, with applications to CT imaging. 38:1-38:46 - Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel:
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. 39:1-39:58 - George H. Chen:
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee. 40:1-40:78 - Naichen Shi, Raed Al Kontar:
Personalized PCA: Decoupling Shared and Unique Features. 41:1-41:82 - Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai:
Invariant and Equivariant Reynolds Networks. 42:1-42:36 - Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu:
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. 43:1-43:44 - Xuran Meng, Jianfeng Yao, Yuan Cao:
Multiple Descent in the Multiple Random Feature Model. 44:1-44:49 - Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta:
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization. 45:1-45:64 - Ville Hyvönen, Elias Jääsaari, Teemu Roos:
A Multilabel Classification Framework for Approximate Nearest Neighbor Search. 46:1-46:51 - Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao:
Efficient Modality Selection in Multimodal Learning. 47:1-47:39 - Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh:
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees. 48:1-48:53 - Ruiqi Zhang, Spencer Frei, Peter L. Bartlett:
Trained Transformers Learn Linear Models In-Context. 49:1-49:55 - Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani:
Resource-Efficient Neural Networks for Embedded Systems. 50:1-50:51 - Chanwoo Park, Ernest K. Ryu:
Optimal First-Order Algorithms as a Function of Inequalities. 51:1-51:66 - Raghav Singal, George Michailidis:
Axiomatic effect propagation in structural causal models. 52:1-52:71 - Dong-Young Lim, Sotirios Sabanis:
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks. 53:1-53:52 - Daniel LeJeune, Jiayu Liu, Reinhard Heckel:
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization. 54:1-54:37 - Ayoub Belhadji, Rémi Gribonval:
Revisiting RIP Guarantees for Sketching Operators on Mixture Models. 55:1-55:68 - Jiang Hu, Kangkang Deng, Jiayuan Wu, Quanzheng Li:
A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity. 56:1-56:32 - Anna C. Neufeld, Ameer Dharamshi, Lucy L. Gao, Daniela M. Witten:
Data Thinning for Convolution-Closed Distributions. 57:1-57:35 - Natalie S. Frank, Jonathan Niles-Weed:
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification. 58:1-58:41 - Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles:
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. 59:1-59:44 - Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang:
Causal-learn: Causal Discovery in Python. 60:1-60:8 - Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar:
Scaling the Convex Barrier with Sparse Dual Algorithms. 61:1-61:51 - Janet van Niekerk, Haavard Rue:
Low-rank Variational Bayes correction to the Laplace method. 62:1-62:25 - Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner:
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates. 63:1-63:60 - Wasim Huleihel, Yehonathan Refael:
Mathematical Framework for Online Social Media Auditing. 64:1-64:40 - Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra, Dimitris Perdios, Sebastian Neumayer, Michael Unser:
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions. 65:1-65:30 - Jeremy Heng, Jeremie Houssineau, Ajay Jasra:
On Unbiased Estimation for Partially Observed Diffusions. 66:1-66:66 - Ariyan Bighashdel, Daan de Geus, Pavol Jancura, Gijs Dubbelman:
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning. 67:1-67:31 - Juan-Pablo Ortega, Daiying Yin:
Learnability of Linear Port-Hamiltonian Systems. 68:1-68:56 - Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee:
Tangential Wasserstein Projections. 69:1-69:41 - Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei:
Scaling Instruction-Finetuned Language Models. 70:1-70:53 - Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup:
Policy Gradient Methods in the Presence of Symmetries and State Abstractions. 71:1-71:57 - Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry:
Pareto Smoothed Importance Sampling. 72:1-72:58 - Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause:
Data Summarization via Bilevel Optimization. 73:1-73:53 - Víctor Peña, Andrés F. Barrientos:
Differentially private methods for managing model uncertainty in linear regression. 74:1-74:44 - Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva, Wei Zhao, Yang Gao, Steffen Eger:
Towards Explainable Evaluation Metrics for Machine Translation. 75:1-75:49 - Jiyuan Tu, Weidong Liu, Xiaojun Mao:
Distributed Estimation on Semi-Supervised Generalized Linear Model. 76:1-76:41 - Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris:
Unlabeled Principal Component Analysis and Matrix Completion. 77:1-77:38 - Kuang-Yao Lee, Lexin Li, Bing Li:
Functional Directed Acyclic Graphs. 78:1-78:48 - Victor Bystrov, Viktoriia Naboka-Krell, Anna Staszewska-Bystrova, Peter Winker:
Choosing the Number of Topics in LDA Models - A Monte Carlo Comparison of Selection Criteria. 79:1-79:30 - Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt:
ptwt - The PyTorch Wavelet Toolbox. 80:1-80:7 - Maksim Velikanov, Dmitry Yarotsky:
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions. 81:1-81:78 - Yicheng Li, Zixiong Yu, Guhan Chen, Qian Lin:
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains. 82:1-82:47 - Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan:
Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training. 83:1-83:74 - Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. 84:1-84:83 - Ricardo Baptista, Rebecca E. Morrison, Olivier Zahm, Youssef M. Marzouk:
Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport. 85:1-85:46 - Wei Luo, Yeying Zhu, Xuekui Zhang, Lin Lin:
A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables. 86:1-86:38 - Michele Peruzzi, David B. Dunson:
Spatial meshing for general Bayesian multivariate models. 87:1-87:49 - Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang:
Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks. 88:1-88:75 - Eliza O'Reilly, Ngoc Mai Tran:
Minimax Rates for High-Dimensional Random Tessellation Forests. 89:1-89:32 - Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy:
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality. 90:1-90:49 - Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar:
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. 91:1-91:85 - Alberto Roverato, Dung Ngoc Nguyen:
Exploration of the Search Space of Gaussian Graphical Models for Paired Data. 92:1-92:41 - Rui Wang, Yuesheng Xu, Mingsong Yan:
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces. 93:1-93:45 - Jiaming Xu, Hanjing Zhu:
Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent. 94:1-94:83 - Tamara Fernandez, Nicolás Rivera:
A General Framework for the Analysis of Kernel-based Tests. 95:1-95:40 - Hans Ulrich Simon, Jan Arne Telle:
MAP- and MLE-Based Teaching. 96:1-96:34 - Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli:
Scaling Speech Technology to 1, 000+ Languages. 97:1-97:52 - Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou:
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization. 98:1-98:52 - Shanshan Song, Yuanyuan Lin, Yong Zhou:
Semi-supervised Inference for Block-wise Missing Data without Imputation. 99:1-99:36 - Nobuaki Kikkawa, Hiroshi Ohno:
Materials Discovery using Max K-Armed Bandit. 100:1-100:40 - Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren:
AMLB: an AutoML Benchmark. 101:1-101:65 - Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai:
Nonparametric Regression for 3D Point Cloud Learning. 102:1-102:56 - Robert C. Williamson, Zac Cranko:
Information Processing Equalities and the Information-Risk Bridge. 103:1-103:53 - Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data. 104:1-104:47 - Roel Bouman, Zaharah Bukhsh, Tom Heskes:
Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? 105:1-105:34 - Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick:
PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design. 106:1-106:26 - Rui Qiu, Zhou Yu, Ruoqing Zhu:
Random Forest Weighted Local Fréchet Regression with Random Objects. 107:1-107:69 - Félix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully:
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. 108:1-108:16 - Zhengdao Chen:
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space. 109:1-109:65 - Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund:
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity. 110:1-110:43 - Nicholas Krämer, Philipp Hennig:
Stable Implementation of Probabilistic ODE Solvers. 111:1-111:29 - Junpei Komiyama, Edouard Fouché, Junya Honda:
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits. 112:1-112:56 - Ömer Deniz Akyildiz, Sotirios Sabanis:
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization. 113:1-113:34 - Jinyan Su, Lijie Hu, Di Wang:
Faster Rates of Differentially Private Stochastic Convex Optimization. 114:1-114:41 - Mingyu Qi, Tianxi Li:
The Non-Overlapping Statistical Approximation to Overlapping Group Lasso. 115:1-115:70 - Yanqing Zhang, Qi Xu, Niansheng Tang, Annie Qu:
Differentially Private Data Release for Mixed-type Data via Latent Factor Models. 116:1-116:37 - Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich:
Functions with average smoothness: structure, algorithms, and learning. 117:1-117:54 - Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi:
Predictive Inference with Weak Supervision. 118:1-118:45 - Yinghua Yao, Yuangang Pan, Jing Li, Ivor W. Tsang, Xin Yao:
Generative Adversarial Ranking Nets. 119:1-119:35 - Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui:
OpenBox: A Python Toolkit for Generalized Black-box Optimization. 120:1-120:11 - Meysam Alishahi, Anna Little, Jeff M. Phillips:
Linear Distance Metric Learning with Noisy Labels. 121:1-121:53 - Guy Kornowski, Ohad Shamir:
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization. 122:1-122:14 - Alexander Dunlap, Jean-Christophe Mourrat:
Sum-of-norms clustering does not separate nearby balls. 123:1-123:40 - Marco Avella Medina, Richard A. Davis, Gennady Samorodnitsky:
Spectral learning of multivariate extremes. 124:1-124:36 - Zihan Zhang, Lei Shi, Ding-Xuan Zhou:
Classification with Deep Neural Networks and Logistic Loss. 125:1-125:117 - Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal:
Random Subgraph Detection Using Queries. 126:1-126:25 - Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice:
Margin-Based Active Learning of Classifiers. 127:1-127:45 - Mochuan Liu, Yuanjia Wang, Haoda Fu, Donglin Zeng:
Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls. 128:1-128:64 - Gan Yuan, Yunfan Zhao, Samory Kpotufe:
Regimes of No Gain in Multi-class Active Learning. 129:1-129:31 - Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu:
Fairness guarantees in multi-class classification with demographic parity. 130:1-130:46 - Jake A. Soloff, Rina Foygel Barber, Rebecca Willett:
Bagging Provides Assumption-free Stability. 131:1-131:35 - Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma:
Representation Learning via Manifold Flattening and Reconstruction. 132:1-132:47 - Katerina Hlavácková-Schindler, Anna Melnykova, Irene Tubikanec:
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length. 133:1-133:26 - Yasuaki Hiraoka, Yusuke Imoto, Théo Lacombe, Killian Meehan, Toshiaki Yachimura:
Topological Node2vec: Enhanced Graph Embedding via Persistent Homology. 134:1-134:26 - Adam Farooq, Yordan P. Raykov, Petar Raykov, Max A. Little:
Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction. 135:1-135:42 - Lucas de Lara, Alberto González-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes:
Transport-based Counterfactual Models. 136:1-136:59 - Etienne Boursier, Vianney Perchet:
A Survey on Multi-player Bandits. 137:1-137:45 - Fanghui Liu, Leello Tadesse Dadi, Volkan Cevher:
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks. 138:1-138:42 - Tomasz Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor:
Fixed points of nonnegative neural networks. 139:1-139:40 - Tengyuan Liang:
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. 140:1-140:27 - Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu:
PyGOD: A Python Library for Graph Outlier Detection. 141:1-141:9 - Xiangxiang Xu, Lizhong Zheng:
Neural Feature Learning in Function Space. 142:1-142:76 - Yutong Wang, Clayton Scott:
Unified Binary and Multiclass Margin-Based Classification. 143:1-143:51 - Idan Attias, Aryeh Kontorovich:
Fat-Shattering Dimension of k-fold Aggregations. 144:1-144:29 - Cheng Zhang, Frederick A. Matsen IV:
A Variational Approach to Bayesian Phylogenetic Inference. 145:1-145:56 - Suprateek Kundu, Joshua Lukemire:
Flexible Bayesian Product Mixture Models for Vector Autoregressions. 146:1-146:52 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti-Anastasia Mastakouri, Dominik Janzing:
DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models. 147:1-147:7 - Yiling Xie, Xiaoming Huo:
Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning. 148:1-148:40 - John J. Cherian, Emmanuel J. Candès:
Statistical Inference for Fairness Auditing. 149:1-149:49 - Yunpeng Zhao, Ning Hao, Ji Zhu:
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks. 150:1-150:42 - Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian:
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. 151:1-151:51 - Raaz Dwivedi, Lester Mackey:
Kernel Thinning. 152:1-152:77 - Maximilian Hüttenrauch, Gerhard Neumann:
Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning. 153:1-153:44 - Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. 154:1-154:50 - Jiading Liu, Lei Shi:
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression. 155:1-155:56 - Hossein Taheri, Christos Thrampoulidis:
Generalization and Stability of Interpolating Neural Networks with Minimal Width. 156:1-156:41 - Rong Tang, Yun Yang:
On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling. 157:1-157:79 - Boyu Li, Robert Simon Fong, Peter Tino:
Simple Cycle Reservoirs are Universal. 158:1-158:28 - Christoph Schultheiss, Peter Bühlmann:
Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models. 159:1-159:41 - Robert Hu, Dino Sejdinovic, Robin J. Evans:
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment. 160:1-160:56 - Xu Liu, Heng Lian, Jian Huang:
More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization. 161:1-161:27 - Isaac Gibbs, Emmanuel J. Candès:
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts. 162:1-162:36 - Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney:
An Analysis of Quantile Temporal-Difference Learning. 163:1-163:47 - Joseph Feldman, Daniel R. Kowal:
Nonparametric Copula Models for Multivariate, Mixed, and Missing Data. 164:1-164:50 - Yunfei Yang, Ding-Xuan Zhou:
Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks. 165:1-165:35 - Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato:
Learning to Warm-Start Fixed-Point Optimization Algorithms. 166:1-166:46 - Yuheng Ma, Hanfang Yang:
Optimal Locally Private Nonparametric Classification with Public Data. 167:1-167:62 - Mingzhang Yin, Yixin Wang, David M. Blei:
Optimization-based Causal Estimation from Heterogeneous Environments. 168:1-168:44 - Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing. 169:1-169:47 - Yang Liu, Yifan Zhou, Ping Li, Feifang Hu:
Cluster-Adaptive Network A/B Testing: From Randomization to Estimation. 170:1-170:48 - Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause:
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning. 171:1-171:54 - Jinchi Chen, Jie Feng, Weiguo Gao, Ke Wei:
Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning. 172:1-172:49 - Taras Bodnar, Nestor Parolya, Erik Thorsén:
Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios. 173:1-173:32 - Yuxin Sun, Dong Lao, Anthony Yezzi, Ganesh Sundaramoorthi:
A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks. 174:1-174:40 - Hengrui Luo, Jeremy E. Purvis, Didong Li:
Spherical Rotation Dimension Reduction with Geometric Loss Functions. 175:1-175:55 - Nicolás García Trillos, Anna V. Little, Daniel McKenzie, James M. Murphy:
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms. 176:1-176:65 - Yiyang Zhao, Linnan Wang, Tian Guo:
Multi-Objective Neural Architecture Search by Learning Search Space Partitions. 177:1-177:41 - Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang:
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization. 178:1-178:62 - Philip M. Long, Peter L. Bartlett:
Sharpness-Aware Minimization and the Edge of Stability. 179:1-179:20 - Thomas Falconer, Jalal Kazempour, Pierre Pinson:
Bayesian Regression Markets. 180:1-180:38 - Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton:
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. 181:1-181:51 - Siddharth Roheda, Hamid Krim, Bo Jiang:
Volterra Neural Networks (VNNs). 182:1-182:29 - Martin Slawski, Bodhisattva Sen:
Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport. 183:1-183:57 - Yuval Belfer, Amnon Geifman, Meirav Galun, Ronen Basri:
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks. 184:1-184:49 - Youngseok Kim, Wei Wang, Peter Carbonetto, Matthew Stephens:
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression. 185:1-185:59 - Junwen Yang, Zixin Zhong, Vincent Y. F. Tan:
Optimal Clustering with Bandit Feedback. 186:1-186:54 - Amir-Reza Asadi:
An Entropy-Based Model for Hierarchical Learning. 187:1-187:45 - Haobo Zhang, Yicheng Li, Qian Lin:
On the Optimality of Misspecified Spectral Algorithms. 188:1-188:50 - Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan:
Differentially Private Topological Data Analysis. 189:1-189:42 - Tian-Yi Zhou, Xiaoming Huo:
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks. 190:1-190:54 - Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang:
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. 191:1-191:61 - Wanli Hong, Shuyang Ling:
Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data. 192:1-192:48 - Lisha Chen, Heshan Devaka Fernando, Yiming Ying, Tianyi Chen:
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance. 193:1-193:53 - Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou:
On the Intrinsic Structures of Spiking Neural Networks. 194:1-194:74 - Yuchen Wu, Kangjie Zhou:
Sharp analysis of power iteration for tensor PCA. 195:1-195:42 - Karl Hajjar, Lénaïc Chizat, Christophe Giraud:
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit. 196:1-196:130 - Mona Azadkia, Fadoua Balabdaoui:
Linear Regression With Unmatched Data: A Deconvolution Perspective. 197:1-197:55 - Naoki Awaya, Li Ma:
Unsupervised Tree Boosting for Learning Probability Distributions. 198:1-198:52 - Bojan Zunkovic, Enej Ilievski:
Grokking phase transitions in learning local rules with gradient descent. 199:1-199:52 - Laixi Shi, Yuejie Chi:
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity. 200:1-200:91 - Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel:
An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters. 201:1-201:33 - Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto:
Manifold Learning by Mixture Models of VAEs for Inverse Problems. 202:1-202:35 - Aleksandr Talitckii, Brendon K. Colbert, Matthew M. Peet:
Efficient Convex Algorithms for Universal Kernel Learning. 203:1-203:40 - Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber:
Dropout Regularization Versus l2-Penalization in the Linear Model. 204:1-204:48 - Shuo-Chieh Huang, Ruey S. Tsay:
Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data. 205:1-205:59 - Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä:
Parallel-in-Time Probabilistic Numerical ODE Solvers. 206:1-206:27 - Christian Fröhlich, Robert C. Williamson:
Risk Measures and Upper Probabilities: Coherence and Stratification. 207:1-207:100 - Arnaud Descours, Arnaud Guillin, Manon Michel, Boris Nectoux:
Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case. 208:1-208:76 - Rage Uday Kiran, Pamalla Veena, Masashi Toyoda, Masaru Kitsuregawa:
PAMI: An Open-Source Python Library for Pattern Mining. 209:1-209:6 - Katharina Proksch, Christoph Alexander Weikamp, Thomas Staudt, Benoît Lelandais, Christophe Zimmer:
From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data. 210:1-210:53 - Yuanxing Chen, Qingzhao Zhang, Shuangge Ma, Kuangnan Fang:
Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis. 211:1-211:60 - Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-trained Gaussian Processes for Bayesian Optimization. 212:1-212:83 - Hannes Köhler:
On the Connection between Lp- and Risk Consistency and its Implications on Regularized Kernel Methods. 213:1-213:33 - José A. Carrillo, Nicolás García Trillos, Sixu Li, Yuhua Zhu:
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization. 214:1-214:51 - Hari Bandi, Dimitris Bertsimas, Thodoris Koukouvinos, Sofie Kupiec:
Interpretable algorithmic fairness in structured and unstructured data. 215:1-215:42 - Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi:
Learning from many trajectories. 216:1-216:109 - Matteo Bettini, Amanda Prorok, Vincent Moens:
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning. 217:1-217:10 - Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Özgür:
Understanding Entropic Regularization in GANs. 218:1-218:32 - Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
A Framework for Improving the Reliability of Black-box Variational Inference. 219:1-219:71 - Lei Shi, Guanghui Wang, Changliang Zou:
Low-Rank Matrix Estimation in the Presence of Change-Points. 220:1-220:71 - Hao Liang, Zhi-Quan Luo:
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds. 221:1-221:56 - Ning Wang, Xin Zhang, Qing Mai:
Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model. 222:1-222:85 - Emilie Chouzenoux, Victor Elvira:
Sparse Graphical Linear Dynamical Systems. 223:1-223:53 - Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao:
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model. 224:1-224:46 - Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano:
Split Conformal Prediction and Non-Exchangeable Data. 225:1-225:38 - Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. 226:1-226:67 - Amin Hosseininasab, Willem-Jan van Hoeve, André A. Ciré:
Memory-Efficient Sequential Pattern Mining with Hybrid Tries. 227:1-227:29 - Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella:
Continuous Prediction with Experts' Advice. 228:1-228:32 - Erich Kummerfeld, Jaewon Lim, Xu Shi:
Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls. 229:1-229:35 - Xiao Han, Y. X. Rachel Wang, Qing Yang, Xin Tong:
Individual-centered Partial Information in Social Networks. 230:1-230:60 - Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak:
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression. 231:1-231:40 - Youssef M. Marzouk, Zhi (Robert) Ren, Sven Wang, Jakob Zech:
Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective. 232:1-232:61 - Shivam Arora, Alex Bihlo, Francis Valiquette:
Invariant Physics-Informed Neural Networks for Ordinary Differential Equations. 233:1-233:24 - Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi:
Regret Analysis of Bilateral Trade with a Smoothed Adversary. 234:1-234:36 - Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone:
Improved Random Features for Dot Product Kernels. 235:1-235:75 - Bin Shi:
On the Hyperparameters in Stochastic Gradient Descent with Momentum. 236:1-236:40 - Thibault Modeste, Clément Dombry:
Characterization of translation invariant MMD on Rd and connections with Wasserstein distances. 237:1-237:39 - Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. 238:1-238:7 - Cameron Jakub, Mihai Nica:
Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization. 239:1-239:45 - Olympio Hacquard, Vadim Lebovici:
Euler Characteristic Tools for Topological Data Analysis. 240:1-240:39 - Liam Madden, Emiliano Dall'Anese, Stephen Becker:
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise. 241:1-241:36 - El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz:
The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis. 242:1-242:76 - Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega:
Memory of recurrent networks: Do we compute it right? 243:1-243:38 - Yiran Zhao, Tiangang Cui:
Tensor-train methods for sequential state and parameter learning in state-space models. 244:1-244:51 - Michele Lohr, Laurent Younes:
FineMorphs: Affine-Diffeomorphic Sequences for Regression. 245:1-245:38 - Shu Hu, George H. Chen:
Fairness in Survival Analysis with Distributionally Robust Optimization. 246:1-246:85 - Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H. S. Torr, Yangchen Pan:
Label Alignment Regularization for Distribution Shift. 247:1-247:32 - Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. 248:1-248:40 - Mario Beraha, Stefano Favaro, Matteo Sesia:
Random measure priors in Bayesian recovery from sketches. 249:1-249:53 - Oscar Hernan Madrid Padilla:
Variance estimation in graphs with the fused lasso. 250:1-250:45 - Paul Escande:
On the Concentration of the Minimizers of Empirical Risks. 251:1-251:53 - Xuetong Li, Jing Zhou, Hansheng Wang:
Gaussian Mixture Models with Rare Events. 252:1-252:40 - Yuan Gao, Jian Huang, Yuling Jiao:
Gaussian Interpolation Flows. 253:1-253:52 - Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie:
PromptBench: A Unified Library for Evaluation of Large Language Models. 254:1-254:22 - Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning. 255:1-255:48
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