Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.

News

2024.02.18: Volume 24 completed; Volume 25 began.
2023.01.20: Volume 23 completed; Volume 24 began.
2022.07.20: New special issue on climate change.
2022.02.18: New blog post: Retrospectives from 20 Years of JMLR .
2022.01.25: Volume 22 completed; Volume 23 began.
2021.12.02: Message from outgoing co-EiC Bernhard Schölkopf.
2021.02.10: Volume 21 completed; Volume 22 began.
More news ...

Latest papers

Aequitas Flow: Streamlining Fair ML Experimentation: Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, Rayid Ghani, 2024. (Machine Learning Open Source Software Paper)
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Information Capacity Regret Bounds for Bandits with Mediator Feedback: Khaled Eldowa, Nicolo Cesa-Bianchi, Alberto Maria Metelli, Marcello Restelli, 2024.
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DAG-Informed Structure Learning from Multi-Dimensional Point Processes: Chunming Zhang, Muhong Gao, Shengji Jia, 2024.
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Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance: Jordan Awan, Aishwarya Ramasethu, 2024.
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A Rainbow in Deep Network Black Boxes: Florentin Guth, Brice Ménard, Gaspar Rochette, Stéphane Mallat, 2024.
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How Two-Layer Neural Networks Learn, One (Giant) Step at a Time: Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan, 2024.
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Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps: Simon Apers, Sander Gribling, Dániel Szilágyi, 2024.
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Memorization With Neural Nets: Going Beyond the Worst Case: Sjoerd Dirksen, Patrick Finke, Martin Genzel, 2024.
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PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates: Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell, 2024.
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Causal effects of intervening variables in settings with unmeasured confounding: Lan Wen, Aaron Sarvet, Mats Stensrud, 2024.
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Lower Complexity Adaptation for Empirical Entropic Optimal Transport: Michel Groppe, Shayan Hundrieser, 2024.
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A Note on Entrywise Consistency for Mixed-data Matrix Completion: Yunxiao Chen, Xiaoou Li, 2024.
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A Characterization of Multioutput Learnability: Vinod Raman, Unique Subedi, Ambuj Tewari, 2024.
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Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning: Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou, 2024.
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Lower Bounds on the Bayesian Risk via Information Measures: Amedeo Roberto Esposito, Adrien Vandenbroucque, Michael Gastpar, 2024.
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Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems: Veronica Vinciotti, Pariya Behrouzi, Reza Mohammadi, 2024.
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Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST): Jimmy Hickey, Jonathan P. Williams, Emily C. Hector, 2024.
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Inference on High-dimensional Single-index Models with Streaming Data: Dongxiao Han, Jinhan Xie, Jin Liu, Liuquan Sun, Jian Huang, Bei Jiang, Linglong Kong, 2024.
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On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport: Minhui Huang, Shiqian Ma, Lifeng Lai, 2024.
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ENNS: Variable Selection, Regression, Classification, and Deep Neural Network for High-Dimensional Data: Kaixu Yang, Arkaprabha Ganguli, Tapabrata Maiti, 2024.
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On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives: Tong Li, Ming Yuan, 2024.
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Open-Source Conversational AI with SpeechBrain 1.0: Mirco Ravanelli, Titouan Parcollet, Adel Moumen, Sylvain de Langen, Cem Subakan, Peter Plantinga, Yingzhi Wang, Pooneh Mousavi, Luca Della Libera, Artem Ploujnikov, Francesco Paissan, Davide Borra, Salah Zaiem, Zeyu Zhao, Shucong Zhang, Georgios Karakasidis, Sung-Lin Yeh, Pierre Champion, Aku Rouhe, Rudolf Braun, Florian Mai, Juan Zuluaga-Gomez, Seyed Mahed Mousavi, Andreas Nautsch, Ha Nguyen, Xuechen Liu, Sangeet Sagar, Jarod Duret, Salima Mdhaffar, Gaëlle Laperrière, Mickael Rouvier, Renato De Mori, Yannick Estève, 2024. (Machine Learning Open Source Software Paper)
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Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components: Naichen Shi, Salar Fattahi, Raed Al Kontar, 2024.
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Generalization on the Unseen, Logic Reasoning and Degree Curriculum: Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk, 2024.
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Goal-Space Planning with Subgoal Models: Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White, 2024.
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Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Constrained Optimization: Enming Liang, Minghua Chen, Steven H. Low, 2024.
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Label Noise Robustness of Conformal Prediction: Bat-Sheva Einbinder, Shai Feldman, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano, 2024.
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PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium: Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang, 2024.
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Geometric Learning with Positively Decomposable Kernels: Nathael Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega, Salem Said, 2024.
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Mentored Learning: Improving Generalization and Convergence of Student Learner: Xiaofeng Cao, Yaming Guo, Heng Tao Shen, Ivor W. Tsang, James T. Kwok, 2024.
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Robust Principal Component Analysis using Density Power Divergence: Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh, 2024.
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Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data: Arhit Chakrabarti, Yang Ni, Ellen Ruth A. Morris, Michael L. Salinas, Robert S. Chapkin, Bani K. Mallick, 2024.
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Stability and L2-penalty in Model Averaging: Hengkun Zhu, Guohua Zou, 2024.
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Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK: Hongru Yang, Ziyu Jiang, Ruizhe Zhang, Yingbin Liang, Zhangyang Wang, 2024.
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Optimal Weighted Random Forests: Xinyu Chen, Dalei Yu, Xinyu Zhang, 2024.
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Efficient Active Manifold Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization: Hao Wang, Ye Wang, Xiangyu Yang, 2024.
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Empirical Design in Reinforcement Learning: Andrew Patterson, Samuel Neumann, Martha White, Adam White, 2024.
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A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators: Neil K. Chada, Quanjun Lang, Fei Lu, Xiong Wang, 2024.
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GGD: Grafting Gradient Descent: Yanjing Feng, Yongdao Zhou, 2024.
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Debiasing Evaluations That Are Biased by Evaluations: Jingyan Wang, Ivan Stelmakh, Yuting Wei, Nihar Shah, 2024.
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Optimal Learning Policies for Differential Privacy in Multi-armed Bandits: Siwei Wang, Jun Zhu, 2024.
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Data-Efficient Policy Evaluation Through Behavior Policy Search: Josiah P. Hanna, Yash Chandak, Philip S. Thomas, Martha White, Peter Stone, Scott Niekum, 2024.
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Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence: Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj, 2024.
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Estimating the Replication Probability of Significant Classification Benchmark Experiments: Daniel Berrar, 2024.
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Causal Discovery with Generalized Linear Models through Peeling Algorithms: Minjie Wang, Xiaotong Shen, Wei Pan, 2024.
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Spectral Regularized Kernel Goodness-of-Fit Tests: Omar Hagrass, Bharath K. Sriperumbudur, Bing Li, 2024.
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Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality: François G. Ged, Maria Han Veiga, 2024.
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Non-Euclidean Monotone Operator Theory and Applications: Alexander Davydov, Saber Jafarpour, Anton V. Proskurnikov, Francesco Bullo, 2024.
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Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates: Hanbaek Lyu, 2024.
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Pure Differential Privacy for Functional Summaries with a Laplace-like Process: Haotian Lin, Matthew Reimherr, 2024.
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Sparse Recovery With Multiple Data Streams: An Adaptive Sequential Testing Approach: Weinan Wang, Bowen Gang, Wenguang Sun, 2024.
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Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning: Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Dingli Ma, Mladen Kolar, Zhaoran Wang, 2024.
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Identifying Causal Eﬀects using Instrumental Time Series: Nuisance IV and Correcting for the Past: Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters, 2024.
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RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control: Jonas Eschmann, Dario Albani, Giuseppe Loianno, 2024. (Machine Learning Open Source Software Paper)
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White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?: Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D. Haeffele, Yi Ma, 2024.
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Commutative Scaling of Width and Depth in Deep Neural Networks: Soufiane Hayou, 2024.
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Value-Distributional Model-Based Reinforcement Learning: Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters, 2024.
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Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries: Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann, 2024.
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PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization: Qiqi Duan, Guochen Zhou, Chang Shao, Zhuowei Wang, Mingyang Feng, Yuwei Huang, Yajing Tan, Yijun Yang, Qi Zhao, Yuhui Shi, 2024. (Machine Learning Open Source Software Paper)
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Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix: Anindya Bhadra, Ksheera Sagar, David Rowe, Sayantan Banerjee, Jyotishka Datta, 2024.
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An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants: Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri, 2024.
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Learning and scoring Gaussian latent variable causal models with unknown additive interventions: Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml, Peter Bühlmann, 2024.
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Non-splitting Neyman-Pearson Classifiers: Jingming Wang, Lucy Xia, Zhigang Bao, Xin Tong, 2024.
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Studying the Interplay between Information Loss and Operation Loss in Representations for Classification: Jorge F. Silva, Felipe Tobar, Mario Vicuña, Felipe Cordova, 2024.
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skscope: Fast Sparsity-Constrained Optimization in Python: Zezhi Wang, Junxian Zhu, Xueqin Wang, Jin Zhu, Huiyang Pen, Peng Chen, Anran Wang, Xiaoke Zhang, 2024. (Machine Learning Open Source Software Paper)
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aeon: a Python Toolkit for Learning from Time Series: Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, Anthony Bagnall, 2024. (Machine Learning Open Source Software Paper)
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Compressed and distributed least-squares regression: convergence rates with applications to federated learning: Constantin Philippenko, Aymeric Dieuleveut, 2024.
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Contamination-source based K-sample clustering: Xavier Milhaud, Denys Pommeret, Yahia Salhi, Pierre Vandekerkhove, 2024.
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Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems: Bokgyeong Kang, John Hughes, Murali Haran, 2024.
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OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research: Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, Yaodong Yang, 2024. (Machine Learning Open Source Software Paper)
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Random Smoothing Regularization in Kernel Gradient Descent Learning: Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao, 2024.
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MLRegTest: A Benchmark for the Machine Learning of Regular Languages: Sam van der Poel, Dakotah Lambert, Kalina Kostyszyn, Tiantian Gao, Rahul Verma, Derek Andersen, Joanne Chau, Emily Peterson, Cody St. Clair, Paul Fodor, Chihiro Shibata, Jeffrey Heinz, 2024.
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A tensor factorization model of multilayer network interdependence: Izabel Aguiar, Dane Taylor, Johan Ugander, 2024.
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Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces: Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy, 2024.
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Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case: Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy, 2024.
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On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression: Oliver Dukes, Stijn Vansteelandt, David Whitney, 2024.
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Deep Neural Network Approximation of Invariant Functions through Dynamical Systems: Qianxiao Li, Ting Lin, Zuowei Shen, 2024.
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A Statistical Experimental Design Method for Constructing Deterministic Sensing Matrices for Compressed Sensing: Youran Qi, Xu He, Tzu-Hsiang Hung, Peter Chien, 2024.
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Functional optimal transport: regularized map estimation and domain adaptation for functional data: Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao, 2024.
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Desiderata for Representation Learning: A Causal Perspective: Yixin Wang, Michael I. Jordan, 2024.
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Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization: Huan Li, Zhouchen Lin, 2024.
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Pearl: A Production-Ready Reinforcement Learning Agent: Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu, 2024. (Machine Learning Open Source Software Paper)
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Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations: David Dalton, Alan Lazarus, Hao Gao, Dirk Husmeier, 2024.
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Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods: Jun Liu, Ye Yuan, 2024.
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False discovery proportion envelopes with m-consistency: Meah Iqraa, Blanchard Gilles, Roquain Etienne, 2024.
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Wasserstein Proximal Coordinate Gradient Algorithms: Rentian Yao, Xiaohui Chen, Yun Yang, 2024.
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Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails: Shaojie Li, Yong Liu, 2024.
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Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies: Boris Hanin, 2024.
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On Regularized Radon-Nikodym Differentiation: Duc Hoan Nguyen, Werner Zellinger, Sergei Pereverzyev, 2024.
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pgmpy: A Python Toolkit for Bayesian Networks: Ankur Ankan, Johannes Textor, 2024. (Machine Learning Open Source Software Paper)
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Recursive Estimation of Conditional Kernel Mean Embeddings: Ambrus Tamás, Balázs Csanád Csáji, 2024.
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Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling: Mert Gurbuzbalaban, Yuanhan Hu, Lingjiong Zhu, 2024.
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Fast Rates in Pool-Based Batch Active Learning: Claudio Gentile, Zhilei Wang, Tong Zhang, 2024.
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On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models: Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu, 2024.
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Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL): Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri, 2024.
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Structured Optimal Variational Inference for Dynamic Latent Space Models: Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick, 2024.
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Stable and Consistent Density-Based Clustering via Multiparameter Persistence: Alexander Rolle, Luis Scoccola, 2024.
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Faster Randomized Methods for Orthogonality Constrained Problems: Boris Shustin, Haim Avron, 2024.
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Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure: Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang, 2024.
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Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning: Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez, 2024.
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PromptBench: A Unified Library for Evaluation of Large Language Models: Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie, 2024. (Machine Learning Open Source Software Paper)
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Gaussian Interpolation Flows: Yuan Gao, Jian Huang, and Yuling Jiao, 2024.
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Gaussian Mixture Models with Rare Events: Xuetong Li, Jing Zhou, Hansheng Wang, 2024.
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On the Concentration of the Minimizers of Empirical Risks: Paul Escande, 2024.
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Variance estimation in graphs with the fused lasso: Oscar Hernan Madrid Padilla, 2024.
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Random measure priors in Bayesian recovery from sketches: Mario Beraha, Stefano Favaro, Matteo Sesia, 2024.
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From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs: Lorenz Richter, Leon Sallandt, Nikolas Nüsken, 2024.
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Label Alignment Regularization for Distribution Shift: Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H.S. Torr, Yangchen Pan, 2024.
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Fairness in Survival Analysis with Distributionally Robust Optimization: Shu Hu, George H. Chen, 2024.
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FineMorphs: Affine-Diffeomorphic Sequences for Regression: Michele Lohr, Laurent Younes, 2024.
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Tensor-train methods for sequential state and parameter learning in state-space models: Yiran Zhao, Tiangang Cui, 2024.
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Memory of recurrent networks: Do we compute it right?: Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega, 2024.
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The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis: El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz, 2024.
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High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise: Liam Madden, Emiliano Dall'Anese, Stephen Becker, 2024.
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Euler Characteristic Tools for Topological Data Analysis: Olympio Hacquard, Vadim Lebovici, 2024.
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Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization: Cameron Jakub, Mihai Nica, 2024.
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Fortuna: A Library for Uncertainty Quantification in Deep Learning: Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau, 2024. (Machine Learning Open Source Software Paper)
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Characterization of translation invariant MMD on Rd and connections with Wasserstein distances: Thibault Modeste, Clément Dombry, 2024.
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On the Hyperparameters in Stochastic Gradient Descent with Momentum: Bin Shi, 2024.
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Improved Random Features for Dot Product Kernels: Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone, 2024.
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Regret Analysis of Bilateral Trade with a Smoothed Adversary: Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi, 2024.
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Invariant Physics-Informed Neural Networks for Ordinary Differential Equations: Shivam Arora, Alex Bihlo, Francis Valiquette, 2024.
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Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective: Youssef Marzouk, Zhi (Robert) Ren, Sven Wang, Jakob Zech, 2024.
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Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression: Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak, 2024.
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Individual-centered Partial Information in Social Networks: Xiao Han, Y. X. Rachel Wang, Qing Yang, Xin Tong, 2024.
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Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls: Erich Kummerfeld, Jaewon Lim, Xu Shi, 2024.
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Continuous Prediction with Experts' Advice: Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella, 2024.
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Memory-Efficient Sequential Pattern Mining with Hybrid Tries: Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire, 2024.
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Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds: Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao, 2024.
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Split Conformal Prediction and Non-Exchangeable Data: Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano, 2024.
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Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model: Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao, 2024.
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Sparse Graphical Linear Dynamical Systems: Emilie Chouzenoux, Victor Elvira, 2024.
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Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model: Ning Wang, Xin Zhang, Qing Mai, 2024.
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Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds: Hao Liang, Zhi-Quan Luo, 2024.
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Low-Rank Matrix Estimation in the Presence of Change-Points: Lei Shi, Guanghui Wang, Changliang Zou, 2024.
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A Framework for Improving the Reliability of Black-box Variational Inference: Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins, 2024.
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Understanding Entropic Regularization in GANs: Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Özgür, 2024.
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BenchMARL: Benchmarking Multi-Agent Reinforcement Learning: Matteo Bettini, Amanda Prorok, Vincent Moens, 2024. (Machine Learning Open Source Software Paper)
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Learning from many trajectories: Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi, 2024.
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Interpretable algorithmic fairness in structured and unstructured data: Hari Bandi, Dimitris Bertsimas, Thodoris Koukouvinos, Sofie Kupiec, 2024.
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FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization: José A. Carrillo, Nicolás García Trillos, Sixu Li, Yuhua Zhu, 2024.
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On the Connection between Lp- and Risk Consistency and its Implications on Regularized Kernel Methods: Hannes Köhler, 2024.
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Pre-trained Gaussian Processes for Bayesian Optimization: Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani, 2024.
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Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis: Yuanxing Chen, Qingzhao Zhang, Shuangge Ma, Kuangnan Fang, 2024.
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From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data: Katharina Proksch, Christoph Alexander Weikamp, Thomas Staudt, Benoit Lelandais, Christophe Zimmer, 2024.
[abs][pdf][bib] [code]

PAMI: An Open-Source Python Library for Pattern Mining: Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case: Arnaud Descours, Arnaud Guillin, Manon Michel, Boris Nectoux, 2024.
[abs][pdf][bib]

Risk Measures and Upper Probabilities: Coherence and Stratification: Christian Fröhlich, Robert C. Williamson, 2024.
[abs][pdf][bib]

Parallel-in-Time Probabilistic Numerical ODE Solvers: Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä, 2024.
[abs][pdf][bib] [code]

Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data: Shuo-Chieh Huang, Ruey S. Tsay, 2024.
[abs][pdf][bib]

Dropout Regularization Versus l2-Penalization in the Linear Model: Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber, 2024.
[abs][pdf][bib]

Efficient Convex Algorithms for Universal Kernel Learning: Aleksandr Talitckii, Brendon Colbert, Matthew M. Peet, 2024.
[abs][pdf][bib] [code]

Manifold Learning by Mixture Models of VAEs for Inverse Problems: Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto, 2024.
[abs][pdf][bib] [code]

An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters: Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel, 2024.
[abs][pdf][bib] [code]

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity: Laixi Shi, Yuejie Chi, 2024.
[abs][pdf][bib] [code]

Grokking phase transitions in learning local rules with gradient descent: Bojan Žunkovič, Enej Ilievski, 2024.
[abs][pdf][bib] [code]

Unsupervised Tree Boosting for Learning Probability Distributions: Naoki Awaya, Li Ma, 2024.
[abs][pdf][bib] [code]

Linear Regression With Unmatched Data: A Deconvolution Perspective: Mona Azadkia, Fadoua Balabdaoui, 2024.
[abs][pdf][bib]

Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit: Karl Hajjar, Lénaïc Chizat, Christophe Giraud, 2024.
[abs][pdf][bib] [code]

Sharp analysis of power iteration for tensor PCA: Yuchen Wu, Kangjie Zhou, 2024.
[abs][pdf][bib]

On the Intrinsic Structures of Spiking Neural Networks: Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou, 2024.
[abs][pdf][bib]

Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance: Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen, 2024.
[abs][pdf][bib] [code]

Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data: Wanli Hong, Shuyang Ling, 2024.
[abs][pdf][bib] [code]

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables: Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang, 2024.
[abs][pdf][bib]

Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks: Tian-Yi Zhou, Xiaoming Huo, 2024.
[abs][pdf][bib]

Differentially Private Topological Data Analysis: Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan, 2024.
[abs][pdf][bib] [code]

On the Optimality of Misspecified Spectral Algorithms: Haobo Zhang, Yicheng Li, Qian Lin, 2024.
[abs][pdf][bib]

An Entropy-Based Model for Hierarchical Learning: Amir R. Asadi, 2024.
[abs][pdf][bib]

Optimal Clustering with Bandit Feedback: Junwen Yang, Zixin Zhong, Vincent Y. F. Tan, 2024.
[abs][pdf][bib]

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression: Youngseok Kim, Wei Wang, Peter Carbonetto, Matthew Stephens, 2024.
[abs][pdf][bib] [code]

Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks: Yuval Belfer, Amnon Geifman, Meirav Galun, Ronen Basri, 2024.
[abs][pdf][bib]

Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport: Martin Slawski, Bodhisattva Sen, 2024.
[abs][pdf][bib]

Volterra Neural Networks (VNNs): Siddharth Roheda, Hamid Krim, Bo Jiang, 2024.
[abs][pdf][bib] [code]

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm: Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton, 2024.
[abs][pdf][bib]

Bayesian Regression Markets: Thomas Falconer, Jalal Kazempour, Pierre Pinson, 2024.
[abs][pdf][bib] [code]

Sharpness-Aware Minimization and the Edge of Stability: Philip M. Long, Peter L. Bartlett, 2024.
[abs][pdf][bib] [code]

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization: Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang, 2024.
[abs][pdf][bib]

Multi-Objective Neural Architecture Search by Learning Search Space Partitions: Yiyang Zhao, Linnan Wang, Tian Guo, 2024.
[abs][pdf][bib] [code]

Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms: Nicolás García Trillos, Anna Little, Daniel McKenzie, James M. Murphy, 2024.
[abs][pdf][bib] [code]

Spherical Rotation Dimension Reduction with Geometric Loss Functions: Hengrui Luo, Jeremy E. Purvis, Didong Li, 2024.
[abs][pdf][bib]

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks: Yuxin Sun, Dong Lao, Anthony Yezzi, Ganesh Sundaramoorthi, 2024.
[abs][pdf][bib] [code]

Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios: Taras Bodnar, Nestor Parolya, Erik Thorsen, 2024.
[abs][pdf][bib]

Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning: Jinchi Chen, Jie Feng, Weiguo Gao, Ke Wei, 2024.
[abs][pdf][bib]

Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning: Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause, 2024.
[abs][pdf][bib] [code]

Cluster-Adaptive Network A/B Testing: From Randomization to Estimation: Yang Liu, Yifan Zhou, Ping Li, Feifang Hu, 2024.
[abs][pdf][bib]

On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing: Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis, 2024.
[abs][pdf][bib]

Optimization-based Causal Estimation from Heterogeneous Environments: Mingzhang Yin, Yixin Wang, David M. Blei, 2024.
[abs][pdf][bib] [code]

Optimal Locally Private Nonparametric Classification with Public Data: Yuheng Ma, Hanfang Yang, 2024.
[abs][pdf][bib] [code]

Learning to Warm-Start Fixed-Point Optimization Algorithms: Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato, 2024.
[abs][pdf][bib] [code]

Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks: Yunfei Yang, Ding-Xuan Zhou, 2024.
[abs][pdf][bib]

Nonparametric Copula Models for Multivariate, Mixed, and Missing Data: Joseph Feldman, Daniel R. Kowal, 2024.
[abs][pdf][bib] [code]

An Analysis of Quantile Temporal-Difference Learning: Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney, 2024.
[abs][pdf][bib]

Conformal Inference for Online Prediction with Arbitrary Distribution Shifts: Isaac Gibbs, Emmanuel J. Candès, 2024.
[abs][pdf][bib] [code]

More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization: Xu Liu, Heng Lian, Jian Huang, 2024.
[abs][pdf][bib]

A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment: Robert Hu, Dino Sejdinovic, Robin J. Evans, 2024.
[abs][pdf][bib] [code]

Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models: Christoph Schultheiss, Peter Bühlmann, 2024.
[abs][pdf][bib] [code]

Simple Cycle Reservoirs are Universal: Boyu Li, Robert Simon Fong, Peter Tino, 2024.
[abs][pdf][bib]

On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling: Rong Tang, Yun Yang, 2024.
[abs][pdf][bib]

Generalization and Stability of Interpolating Neural Networks with Minimal Width: Hossein Taheri, Christos Thrampoulidis, 2024.
[abs][pdf][bib]

Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression: Jiading Liu, Lei Shi, 2024.
[abs][pdf][bib]

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations: Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao, 2024.
[abs][pdf][bib]

Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning: Maximilian Hüttenrauch, Gerhard Neumann, 2024.
[abs][pdf][bib]

Kernel Thinning: Raaz Dwivedi, Lester Mackey, 2024.
[abs][pdf][bib] [code]

Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions: Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian, 2024.
[abs][pdf][bib]

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks: Yunpeng Zhao, Ning Hao, Ji Zhu, 2024.
[abs][pdf][bib]

Statistical Inference for Fairness Auditing: John J. Cherian, Emmanuel J. Candès, 2024.
[abs][pdf][bib] [code]

Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning: Yiling Xie, Xiaoming Huo, 2024.
[abs][pdf][bib]

DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models: Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Flexible Bayesian Product Mixture Models for Vector Autoregressions: Suprateek Kundu, Joshua Lukemire, 2024.
[abs][pdf][bib]

A Variational Approach to Bayesian Phylogenetic Inference: Cheng Zhang, Frederick A. Matsen IV, 2024.
[abs][pdf][bib] [code]

Fat-Shattering Dimension of k-fold Aggregations: Idan Attias, Aryeh Kontorovich, 2024.
[abs][pdf][bib]

Unified Binary and Multiclass Margin-Based Classification: Yutong Wang, Clayton Scott, 2024.
[abs][pdf][bib]

Neural Feature Learning in Function Space: Xiangxiang Xu, Lizhong Zheng, 2024.
[abs][pdf][bib] [code]

PyGOD: A Python Library for Graph Outlier Detection: Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria: Tengyuan Liang, 2024.
[abs][pdf][bib]

Fixed points of nonnegative neural networks: Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor, 2024.
[abs][pdf][bib] [code]

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks: Fanghui Liu, Leello Dadi, Volkan Cevher, 2024.
[abs][pdf][bib]

A Survey on Multi-player Bandits: Etienne Boursier, Vianney Perchet, 2024.
[abs][pdf][bib]

Transport-based Counterfactual Models: Lucas De Lara, Alberto González-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes, 2024.
[abs][pdf][bib] [code]

Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction: Adam Farooq, Yordan P. Raykov, Petar Raykov, Max A. Little, 2024.
[abs][pdf][bib] [code]

Topological Node2vec: Enhanced Graph Embedding via Persistent Homology: Yasuaki Hiraoka, Yusuke Imoto, Théo Lacombe, Killian Meehan, Toshiaki Yachimura, 2024.
[abs][pdf][bib] [code]

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length: Katerina Hlaváčková-Schindler, Anna Melnykova, Irene Tubikanec, 2024.
[abs][pdf][bib] [code]

Representation Learning via Manifold Flattening and Reconstruction: Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma, 2024.
[abs][pdf][bib] [code]

Bagging Provides Assumption-free Stability: Jake A. Soloff, Rina Foygel Barber, Rebecca Willett, 2024.
[abs][pdf][bib] [code]

Fairness guarantees in multi-class classification with demographic parity: Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu, 2024.
[abs][pdf][bib]

Regimes of No Gain in Multi-class Active Learning: Gan Yuan, Yunfan Zhao, Samory Kpotufe, 2024.
[abs][pdf][bib]

Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls: Mochuan Liu, Yuanjia Wang, Haoda Fu, Donglin Zeng, 2024.
[abs][pdf][bib]

Margin-Based Active Learning of Classifiers: Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, 2024.
[abs][pdf][bib]

Random Subgraph Detection Using Queries: Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal, 2024.
[abs][pdf][bib]

Classification with Deep Neural Networks and Logistic Loss: Zihan Zhang, Lei Shi, Ding-Xuan Zhou, 2024.
[abs][pdf][bib]

Spectral learning of multivariate extremes: Marco Avella Medina, Richard A Davis, Gennady Samorodnitsky, 2024.
[abs][pdf][bib]

Sum-of-norms clustering does not separate nearby balls: Alexander Dunlap, Jean-Christophe Mourrat, 2024.
[abs][pdf][bib] [code]

An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization: Guy Kornowski, Ohad Shamir, 2024.
[abs][pdf][bib]

Linear Distance Metric Learning with Noisy Labels: Meysam Alishahi, Anna Little, Jeff M. Phillips, 2024.
[abs][pdf][bib] [code]

OpenBox: A Python Toolkit for Generalized Black-box Optimization: Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Generative Adversarial Ranking Nets: Yinghua Yao, Yuangang Pan, Jing Li, Ivor W. Tsang, Xin Yao, 2024.
[abs][pdf][bib] [code]

Predictive Inference with Weak Supervision: Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi, 2024.
[abs][pdf][bib]

Functions with average smoothness: structure, algorithms, and learning: Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich, 2024.
[abs][pdf][bib]

Differentially Private Data Release for Mixed-type Data via Latent Factor Models: Yanqing Zhang, Qi Xu, Niansheng Tang, Annie Qu, 2024.
[abs][pdf][bib]

The Non-Overlapping Statistical Approximation to Overlapping Group Lasso: Mingyu Qi, Tianxi Li, 2024.
[abs][pdf][bib] [code]

Faster Rates of Differentially Private Stochastic Convex Optimization: Jinyan Su, Lijie Hu, Di Wang, 2024.
[abs][pdf][bib]

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization: O. Deniz Akyildiz, Sotirios Sabanis, 2024.
[abs][pdf][bib]

Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits: Junpei Komiyama, Edouard Fouché, Junya Honda, 2024.
[abs][pdf][bib] [code]

Stable Implementation of Probabilistic ODE Solvers: Nicholas Krämer, Philipp Hennig, 2024.
[abs][pdf][bib]

More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity: Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund, 2024.
[abs][pdf][bib]

Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space: Zhengdao Chen, 2024.
[abs][pdf][bib]

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration: Felix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Random Forest Weighted Local Fréchet Regression with Random Objects: Rui Qiu, Zhou Yu, Ruoqing Zhu, 2024.
[abs][pdf][bib] [code]

PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design: Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick, 2024.
[abs][pdf][bib] [code]

Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?: Roel Bouman, Zaharah Bukhsh, Tom Heskes, 2024.
[abs][pdf][bib] [code]

Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data: Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini, 2024.
[abs][pdf][bib]

Information Processing Equalities and the Information–Risk Bridge: Robert C. Williamson, Zac Cranko, 2024.
[abs][pdf][bib]

Nonparametric Regression for 3D Point Cloud Learning: Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai, 2024.
[abs][pdf][bib] [code]

AMLB: an AutoML Benchmark: Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren, 2024.
[abs][pdf][bib] [code]

Materials Discovery using Max K-Armed Bandit: Nobuaki Kikkawa, Hiroshi Ohno, 2024.
[abs][pdf][bib]

Semi-supervised Inference for Block-wise Missing Data without Imputation: Shanshan Song, Yuanyuan Lin, Yong Zhou, 2024.
[abs][pdf][bib]

Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization: Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou, 2024.
[abs][pdf][bib]

Scaling Speech Technology to 1,000+ Languages: 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, 2024.
[abs][pdf][bib] [code]

MAP- and MLE-Based Teaching: Hans Ulrich Simon, Jan Arne Telle, 2024.
[abs][pdf][bib]

A General Framework for the Analysis of Kernel-based Tests: Tamara Fernández, Nicolás Rivera, 2024.
[abs][pdf][bib]

Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent: Jiaming Xu, Hanjing Zhu, 2024.
[abs][pdf][bib]

Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces: Rui Wang, Yuesheng Xu, Mingsong Yan, 2024.
[abs][pdf][bib]

Exploration of the Search Space of Gaussian Graphical Models for Paired Data: Alberto Roverato, Dung Ngoc Nguyen, 2024.
[abs][pdf][bib]

The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective: Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar, 2024.
[abs][pdf][bib] [code]

Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality: Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy, 2024.
[abs][pdf][bib]

Minimax Rates for High-Dimensional Random Tessellation Forests: Eliza O'Reilly, Ngoc Mai Tran, 2024.
[abs][pdf][bib]

Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks: Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang, 2024.
[abs][pdf][bib]

Spatial meshing for general Bayesian multivariate models: Michele Peruzzi, David B. Dunson, 2024.
[abs][pdf][bib] [code]

A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables: Wei Luo, Yeying Zhu, Xuekui Zhang, Lin Lin, 2024.
[abs][pdf][bib]

Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport: Ricardo Baptista, Rebecca Morrison, Olivier Zahm, Youssef Marzouk, 2024.
[abs][pdf][bib] [code]

On the Learnability of Out-of-distribution Detection: Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu, 2024.
[abs][pdf][bib]

Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training: Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan, 2024.
[abs][pdf][bib] [code]

On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains: Yicheng Li, Zixiong Yu, Guhan Chen, Qian Lin, 2024.
[abs][pdf][bib]

Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions: Maksim Velikanov, Dmitry Yarotsky, 2024.
[abs][pdf][bib]

ptwt - The PyTorch Wavelet Toolbox: Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria: Victor Bystrov, Viktoriia Naboka-Krell, Anna Staszewska-Bystrova, Peter Winker, 2024.
[abs][pdf][bib] [code]

Functional Directed Acyclic Graphs: Kuang-Yao Lee, Lexin Li, Bing Li, 2024.
[abs][pdf][bib]

Unlabeled Principal Component Analysis and Matrix Completion: Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris, 2024.
[abs][pdf][bib] [code]

Distributed Estimation on Semi-Supervised Generalized Linear Model: Jiyuan Tu, Weidong Liu, Xiaojun Mao, 2024.
[abs][pdf][bib]

Towards Explainable Evaluation Metrics for Machine Translation: Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva, Wei Zhao, Yang Gao, Steffen Eger, 2024.
[abs][pdf][bib]

Differentially private methods for managing model uncertainty in linear regression: Víctor Peña, Andrés F. Barrientos, 2024.
[abs][pdf][bib]

Data Summarization via Bilevel Optimization: Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause, 2024.
[abs][pdf][bib]

Pareto Smoothed Importance Sampling: Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry, 2024.
[abs][pdf][bib] [code]

Policy Gradient Methods in the Presence of Symmetries and State Abstractions: Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup, 2024.
[abs][pdf][bib] [code]

Scaling Instruction-Finetuned Language Models: 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 Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei, 2024.
[abs][pdf][bib]

Tangential Wasserstein Projections: Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee, 2024.
[abs][pdf][bib] [code]

Learnability of Linear Port-Hamiltonian Systems: Juan-Pablo Ortega, Daiying Yin, 2024.
[abs][pdf][bib] [code]

Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning: Ariyan Bighashdel, Daan de Geus, Pavol Jancura, Gijs Dubbelman, 2024.
[abs][pdf][bib] [code]

On Unbiased Estimation for Partially Observed Diffusions: Jeremy Heng, Jeremie Houssineau, Ajay Jasra, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions: Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra, Dimitris Perdios, Sebastian Neumayer, Michael Unser, 2024.
[abs][pdf][bib] [code]

Mathematical Framework for Online Social Media Auditing: Wasim Huleihel, Yehonathan Refael, 2024.
[abs][pdf][bib]

An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates: Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner, 2024.
[abs][pdf][bib]

Low-rank Variational Bayes correction to the Laplace method: Janet van Niekerk, Haavard Rue, 2024.
[abs][pdf][bib] [code]

Scaling the Convex Barrier with Sparse Dual Algorithms: Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar, 2024.
[abs][pdf][bib] [code]

Causal-learn: Causal Discovery in Python: Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics: Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles, 2024.
[abs][pdf][bib] [code]

Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification: Natalie S. Frank, Jonathan Niles-Weed, 2024.
[abs][pdf][bib]

Data Thinning for Convolution-Closed Distributions: Anna Neufeld, Ameer Dharamshi, Lucy L. Gao, Daniela Witten, 2024.
[abs][pdf][bib] [code]

A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity: Jiang Hu, Kangkang Deng, Jiayuan Wu, Quanzheng Li, 2024.
[abs][pdf][bib]

Revisiting RIP Guarantees for Sketching Operators on Mixture Models: Ayoub Belhadji, Rémi Gribonval, 2024.
[abs][pdf][bib]

Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization: Daniel LeJeune, Jiayu Liu, Reinhard Heckel, 2024.
[abs][pdf][bib] [code]

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks: Dong-Young Lim, Sotirios Sabanis, 2024.
[abs][pdf][bib]

Axiomatic effect propagation in structural causal models: Raghav Singal, George Michailidis, 2024.
[abs][pdf][bib]

Optimal First-Order Algorithms as a Function of Inequalities: Chanwoo Park, Ernest K. Ryu, 2024.
[abs][pdf][bib] [code]

Resource-Efficient Neural Networks for Embedded Systems: Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani, 2024.
[abs][pdf][bib]

Trained Transformers Learn Linear Models In-Context: Ruiqi Zhang, Spencer Frei, Peter L. Bartlett, 2024.
[abs][pdf][bib]

Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees: Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh, 2024.
[abs][pdf][bib]

Efficient Modality Selection in Multimodal Learning: Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao, 2024.
[abs][pdf][bib]

A Multilabel Classification Framework for Approximate Nearest Neighbor Search: Ville Hyvönen, Elias Jääsaari, Teemu Roos, 2024.
[abs][pdf][bib] [code]

Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization: Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta, 2024.
[abs][pdf][bib] [code]

Multiple Descent in the Multiple Random Feature Model: Xuran Meng, Jianfeng Yao, Yuan Cao, 2024.
[abs][pdf][bib]

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling: Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu, 2024.
[abs][pdf][bib]

Invariant and Equivariant Reynolds Networks: Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Personalized PCA: Decoupling Shared and Unique Features: Naichen Shi, Raed Al Kontar, 2024.
[abs][pdf][bib] [code]

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee: George H. Chen, 2024.
[abs][pdf][bib] [code]

On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control: Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel, 2024.
[abs][pdf][bib]

Convergence for nonconvex ADMM, with applications to CT imaging: Rina Foygel Barber, Emil Y. Sidky, 2024.
[abs][pdf][bib] [code]

Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms: T. Tony Cai, Hongji Wei, 2024.
[abs][pdf][bib]

Sparse NMF with Archetypal Regularization: Computational and Robustness Properties: Kayhan Behdin, Rahul Mazumder, 2024.
[abs][pdf][bib] [code]

Deep Network Approximation: Beyond ReLU to Diverse Activation Functions: Shijun Zhang, Jianfeng Lu, Hongkai Zhao, 2024.
[abs][pdf][bib]

Effect-Invariant Mechanisms for Policy Generalization: Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters, 2024.
[abs][pdf][bib]

Pygmtools: A Python Graph Matching Toolkit: 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, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Heterogeneous-Agent Reinforcement Learning: Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang, 2024.
[abs][pdf][bib] [code]

Sample-efficient Adversarial Imitation Learning: Dahuin Jung, Hyungyu Lee, Sungroh Yoon, 2024.
[abs][pdf][bib]

Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent: Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi, 2024.
[abs][pdf][bib]

Rates of convergence for density estimation with generative adversarial networks: Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov, 2024.
[abs][pdf][bib]

Additive smoothing error in backward variational inference for general state-space models: Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff, 2024.
[abs][pdf][bib]

Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality: Stephan Wojtowytsch, 2024.
[abs][pdf][bib]

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees: Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge, 2024.
[abs][pdf][bib] [code]

On Tail Decay Rate Estimation of Loss Function Distributions: Etrit Haxholli, Marco Lorenzi, 2024.
[abs][pdf][bib] [code]

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces: Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao, 2024.
[abs][pdf][bib]

Post-Regularization Confidence Bands for Ordinary Differential Equations: Xiaowu Dai, Lexin Li, 2024.
[abs][pdf][bib]

On the Generalization of Stochastic Gradient Descent with Momentum: Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang, 2024.
[abs][pdf][bib]

Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension: Shotaro Yagishita, Jun-ya Gotoh, 2024.
[abs][pdf][bib]

Iterate Averaging in the Quest for Best Test Error: Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts, 2024.
[abs][pdf][bib] [code]

Nonparametric Inference under B-bits Quantization: Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang, 2024.
[abs][pdf][bib]

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box: Ryan Giordano, Martin Ingram, Tamara Broderick, 2024.
[abs][pdf][bib] [code]

On Sufficient Graphical Models: Bing Li, Kyongwon Kim, 2024.
[abs][pdf][bib]

Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond: Nathan Kallus, Xiaojie Mao, Masatoshi Uehara, 2024.
[abs][pdf][bib] [code]

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks: Sebastian Neumayer, Lénaïc Chizat, Michael Unser, 2024.
[abs][pdf][bib]

Improving physics-informed neural networks with meta-learned optimization: Alex Bihlo, 2024.
[abs][pdf][bib]

A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent: Stefan Ankirchner, Stefan Perko, 2024.
[abs][pdf][bib]

Critically Assessing the State of the Art in Neural Network Verification: Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn, 2024.
[abs][pdf][bib]

Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design: Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov, 2024.
[abs][pdf][bib]

Modeling Random Networks with Heterogeneous Reciprocity: Daniel Cirkovic, Tiandong Wang, 2024.
[abs][pdf][bib]

Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment: Zixian Yang, Xin Liu, Lei Ying, 2024.
[abs][pdf][bib]

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models: Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh, 2024.
[abs][pdf][bib] [code]

Decorrelated Variable Importance: Isabella Verdinelli, Larry Wasserman, 2024.
[abs][pdf][bib]

Model-Free Representation Learning and Exploration in Low-Rank MDPs: Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, 2024.
[abs][pdf][bib]

Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method: Ernesto Araya, Guillaume Braun, Hemant Tyagi, 2024.
[abs][pdf][bib] [code]

Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization: Shicong Cen, Yuting Wei, Yuejie Chi, 2024.
[abs][pdf][bib]

Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic: Zheng Tracy Ke, Jun S. Liu, Yucong Ma, 2024.
[abs][pdf][bib]

Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction: Yuze Han, Guangzeng Xie, Zhihua Zhang, 2024.
[abs][pdf][bib]

On Truthing Issues in Supervised Classification: Jonathan K. Su, 2024.
[abs][pdf][bib]

Full list