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

2025.02.10: Volume 25 completed; Volume 26 began.
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

Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin: Akshay Kumar, Jarvis Haupt, 2025.
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Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity: Qiankun Shi, Jie Peng, Kun Yuan, Xiao Wang, Qing Ling, 2025.
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Towards Unified Native Spaces in Kernel Methods: Xavier Emery, Emilio Porcu, Moreno Bevilacqua, 2025.
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TorchCP: A Python Library for Conformal Prediction: Jianguo Huang, Jianqing Song, Xuanning Zhou, Bingyi Jing, Hongxin Wei, 2025. (Machine Learning Open Source Software Paper)
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Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval: Saul Santos, Vlad Niculae, Daniel McNamee, Andre F.T. Martins, 2025.
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Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models: Juraj Bodik, Valérie Chavez-Demoulin, 2025.
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Fundamental Limits of Membership Inference Attacks on Machine Learning Models: Eric Aubinais, Elisabeth Gassiat, Pablo Piantanida, 2025.
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On the Robustness of Kernel Goodness-of-Fit Tests: Xing Liu, François-Xavier Briol, 2025.
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Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition: Zhenting Luan, Defeng Sun, Haoning Wang, Liping Zhang, 2025.
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Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction: Jake Roth, Ying Cui, 2025.
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Collaborative likelihood-ratio estimation over graphs: Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos, 2025.
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On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent: Rahul Singh, Abhinek Shukla, Dootika Vats, 2025.
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Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies: Zhanyu Wang, Guang Cheng, Jordan Awan, 2025.
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Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs: Dongsheng Ding, Kaiqing Zhang, Jiali Duan, Tamer Basar, Mihailo R. Jovanovic, 2025.
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Differentially Private Multivariate Medians: Kelly Ramsay, Aukosh Jagannath, Shoja'eddin Chenouri, 2025.
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VFOSA: Variance-Reduced Fast Operator Splitting Algorithms for Generalized Equations: Quoc Tran-Dinh, 2025.
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Scaling Capability in Token Space: An Analysis of Large Vision Language Model: Tenghui Li, Guoxu Zhou, Xuyang Zhao, Qibin Zhao, 2025.
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Minimax Optimal Two-Sample Testing under Local Differential Privacy: Jongmin Mun, Seungwoo Kwak, Ilmun Kim, 2025.
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Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds: Nathanaël Munier, Emmanuel Soubies, Pierre Weiss, 2025.
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An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models: Tong Xu, Simge Küçükyavuz, Ali Shojaie, Armeen Taeb, 2025.
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Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation: David Holzmüller, Francis Bach, 2025.
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A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning: Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton, 2025.
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Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection: Nikita Zozoulenko, Thomas Cass, Lukas Gonon, 2025.
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Stable learning using spiking neural networks equipped with affine encoders and decoders: A. Martina Neuman, Dominik Dold, Philipp Christian Petersen, 2025.
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Efficient Knowledge Deletion from Trained Models Through Layer-wise Partial Machine Unlearning: Vinay Chakravarthi Gogineni, Esmaeil S. Nadimi, 2025.
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General Loss Functions Lead to (Approximate) Interpolation in High Dimensions: Kuo-Wei Lai, Vidya Muthukumar, 2025.
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Piecewise deterministic sampling with splitting schemes: Andrea Bertazzi, Paul Dobson, Pierre Monmarché, 2025.
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Hierarchical and Stochastic Crystallization Learning: Geometrically Leveraged Nonparametric Regression with Delaunay Triangulation: Jiaqi Gu, Guosheng Yin, 2025.
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Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2: Yuri Chervonyi, Trieu H. Trinh, Miroslav Olšák, Xiaomeng Yang, Hoang H. Nguyen, Marcelo Menegali, Junehyuk Jung, Junsu Kim, Vikas Verma, Quoc V. Le, Thang Luong, 2025.
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Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity: Boao Kong, Shuchen Zhu, Songtao Lu, Xinmeng Huang, Kun Yuan, 2025.
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Fair Text Classification via Transferable Representations: Thibaud Leteno, Michael Perrot, Charlotte Laclau, Antoine Gourru, Christophe Gravier, 2025.
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Stochastic Interior-Point Methods for Smooth Conic Optimization with Applications: Chuan He, Zhanwang Deng, 2025.
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Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration: Tao Sun, Xinwang Liu, Kun Yuan, 2025.
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Generalized multi-view model: Adaptive density estimation under low-rank constraints: Julien Chhor, Olga Klopp, Alexandre B. Tsybakov, 2025.
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(De)-regularized Maximum Mean Discrepancy Gradient Flow: Zonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. Sriperumbudur, 2025.
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On Probabilistic Embeddings in Optimal Dimension Reduction: Ryan Murray, Adam Pickarski, 2025.
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Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN: Subhajit Patra, Sonali Panda, Bikram Keshari Parida, Mahima Arya, Kurt Jacobs, Denys I. Bondar, Abhijit Sen, 2025.
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Graph-accelerated Markov Chain Monte Carlo using Approximate Samples: Leo L. Duan, Anirban Bhattacharya, 2025.
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Online Quantile Regression: Yinan Shen, Dong Xia, Wen-Xin Zhou, 2025.
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Statistical Inference of Random Graphs With a Surrogate Likelihood Function: Dingbo Wu, Fangzheng Xie, 2025.
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On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference: Zhuangyan Fang, Ruiqi Zhao, Yue Liu, Yangbo He, 2025.
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An Augmentation Overlap Theory of Contrastive Learning: Qi Zhang, Yifei Wang, Yisen Wang, 2025.
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Algorithms for ridge estimation with convergence guarantees: Wanli Qiao, Wolfgang Polonik, 2025.
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Talent: A Tabular Analytics and Learning Toolbox: Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, Huai-Hong Yin, Tao Zhou, Jun-Peng Jiang, Han-Jia Ye, 2025. (Machine Learning Open Source Software Paper)
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Inferring Change Points in High-Dimensional Regression via Approximate Message Passing: Gabriel Arpino, Xiaoqi Liu, Julia Gontarek, Ramji Venkataramanan, 2025.
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Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime: Parthe Pandit, Zhichao Wang, Yizhe Zhu, 2025.
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Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound: Bo Xue, Ji Cheng, Fei Liu, Yimu Wang, Lijun Zhang, Qingfu Zhang, 2025.
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On the Natural Gradient of the Evidence Lower Bound: Nihat Ay, Jesse van Oostrum, Adwait Datar, 2025.
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Geometry and Stability of Supervised Learning Problems: Facundo Mémoli, Brantley Vose, Robert C. Williamson, 2025.
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Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination: Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano, Wei Hu, Qing Qu, 2025.
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Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions: Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin, 2025.
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A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning: Stephen M. S. Lee, Mehdi Soleymani, 2025.
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Scalable and Adaptive Variational Bayes Methods for Hawkes Processes: Deborah Sulem, Vincent Rivoirard, Judith Rousseau, 2025.
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Biological Sequence Kernels with Guaranteed Flexibility: Alan N. Amin, Debora S. Marks, Eli N. Weinstein, 2025.
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Unified Discrete Diffusion for Categorical Data: Lingxiao Zhao, Xueying Ding, Lijun Yu, Leman Akoglu, 2025.
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Reinforcement Learning for Infinite-Dimensional Systems: Wei Zhang, Jr-Shin Li, 2025.
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Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation: Hao Liu, Jiahui Cheng, Wenjing Liao, 2025.
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Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints: Kazumi Kasaura, 2025.
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Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation: Michael Sucker, Jalal Fadili, Peter Ochs, 2025.
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Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data: Hee Cheol Chung, Yang Ni, Irina Gaynanova, 2025.
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Stochastic Interpolants: A Unifying Framework for Flows and Diffusions: Michael Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, 2025.
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Efficient Methods for Non-stationary Online Learning: Peng Zhao, Yan-Feng Xie, Lijun Zhang, Zhi-Hua Zhou, 2025.
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Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration: Adel Nabli, Edouard Oyallon, 2025.
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Mixtures of Gaussian Process Experts with SMC^2: Teemu Härkönen, Sara Wade, Kody Law, Lassi Roininen, 2025.
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Robust Point Matching with Distance Profiles: YoonHaeng Hur, Yuehaw Khoo, 2025.
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BoFire: Bayesian Optimization Framework Intended for Real Experiments: Johannes P. Dürholt, Thomas S. Asche, Johanna Kleinekorte, Gabriel Mancino-Ball, Benjamin Schiller, Simon Sung, Julian Keupp, Aaron Osburg, Toby Boyne, Ruth Misener, Rosona Eldred, Chrysoula Kappatou, Robert M. Lee, Dominik Linzner, Wagner Steuer Costa, David Walz, Niklas Wulkow, Behrang Shafei, 2025. (Machine Learning Open Source Software Paper)
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Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection: Chengde Qian, Guanghui Wang, Changliang Zou, 2025.
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Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs: Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul, 2025.
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Are Ensembles Getting Better All the Time?: Pierre-Alexandre Mattei, Damien Garreau, 2025.
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An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition: Qihang Lin, Negar Soheili, Runchao Ma, Selvaprabu Nadarajah, 2025.
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Operator Learning for Hyperbolic PDEs: Christopher Wang, Alex Townsend, 2025.
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Optimal subsampling for high-dimensional partially linear models via machine learning methods: Yujing Shao, Lei Wang, Heng Lian, Haiying Wang, 2025.
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Decentralized Sparse Linear Regression via Gradient-Tracking: Marie Maros, Gesualdo Scutari, Ying Sun, Guang Cheng, 2025.
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Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty: Yujin Jeong, Dominik Rothenhäusler, 2025.
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Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization: Sébastien J. Petit, Julien Bect, Emmanuel Vazquez, 2025.
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Linear Separation Capacity of Self-Supervised Representation Learning: Shulei Wang, 2025.
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On the Convergence of Projected Policy Gradient for Any Constant Step Sizes: Jiacai Liu, Wenye Li, Dachao Lin, Ke Wei, Zhihua Zhang, 2025.
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Learning with Linear Function Approximations in Mean-Field Control: Erhan Bayraktar, Ali Devran Kara, 2025.
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A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization: Junwen Qiu, Xiao Li, Andre Milzarek, 2025.
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Model-free Change-Point Detection Using AUC of a Classifier: Rohit Kanrar, Feiyu Jiang, Zhanrui Cai, 2025.
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EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback: Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik, 2025.
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Multiple Instance Verification: Xin Xu, Eibe Frank, Geoffrey Holmes, 2025.
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Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness: Ye Tian, Yuqi Gu, Yang Feng, 2025.
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Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data: Yanxin Jin, Yang Ning, Kean Ming Tan, 2025.
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Optimizing Return Distributions with Distributional Dynamic Programming: Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney, 2025.
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Imprecise Multi-Armed Bandits: Representing Irreducible Uncertainty as a Zero-Sum Game: Vanessa Kosoy, 2025.
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Early Alignment in Two-Layer Networks Training is a Two-Edged Sword: Etienne Boursier, Nicolas Flammarion, 2025.
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Hierarchical Decision Making Based on Structural Information Principles: Xianghua Zeng, Hao Peng, Dingli Su, Angsheng Li, 2025.
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Generative Adversarial Networks: Dynamics: Matias G. Delgadino, Bruno B. Suassuna, Rene Cabrera, 2025.
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"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts: Varun Babbar*, Zhicheng Guo*, Cynthia Rudin, 2025.
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Assumption-lean and data-adaptive post-prediction inference: Jiacheng Miao, Xinran Miao, Yixuan Wu, Jiwei Zhao, Qiongshi Lu, 2025.
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Bagged Regularized k-Distances for Anomaly Detection: Yuchao Cai, Hanfang Yang, Yuheng Ma, Hanyuan Hang, 2025.
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Four Axiomatic Characterizations of the Integrated Gradients Attribution Method: Daniel Lundstrom, Meisam Razaviyayn, 2025.
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Fast Algorithm for Constrained Linear Inverse Problems: Mohammed Rayyan Sheriff, Floor Fenne Redel, Peyman Mohajerin Esfahani, 2025.
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High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces: Shihao Shao, Yikang Li, Zhouchen Lin, Qinghua Cui, 2025.
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Best Linear Unbiased Estimate from Privatized Contingency Tables: Jordan Awan, Adam Edwards, Paul Bartholomew, Andrew Sillers, 2025.
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Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data: Thomas Chen, Patrícia Muñoz Ewald, 2025.
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Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods: Bertille FOLLAIN, Francis BACH, 2025.
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Data-Driven Performance Guarantees for Classical and Learned Optimizers: Rajiv Sambharya, Bartolomeo Stellato, 2025.
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Contextual Bandits with Stage-wise Constraints: Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, 2025.
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Boosting Causal Additive Models: Maximilian Kertel, Nadja Klein, 2025.
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Frequentist Guarantees of Distributed (Non)-Bayesian Inference: Bohan Wu, César A. Uribe, 2025.
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Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection: Daiqi Gao, Yufeng Liu, Donglin Zeng, 2025.
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EMaP: Explainable AI with Manifold-based Perturbations: Minh Nhat Vu, Huy Quang Mai, My T. Thai, 2025.
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Autoencoders in Function Space: Justin Bunker, Mark Girolami, Hefin Lambley, Andrew M. Stuart, T. J. Sullivan, 2025.
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Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds: Paul Rosa, Judith Rousseau, 2025.
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System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning: Matteo Bettini, Ajay Shankar, Amanda Prorok, 2025.
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Distribution Estimation under the Infinity Norm: Aryeh Kontorovich, Amichai Painsky, 2025.
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Extending Temperature Scaling with Homogenizing Maps: Christopher Qian, Feng Liang, Jason Adams, 2025.
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Density Estimation Using the Perceptron: Patrik Róbert Gerber, Tianze Jiang, Yury Polyanskiy, Rui Sun, 2025.
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Simplex Constrained Sparse Optimization via Tail Screening: Peng Chen, Jin Zhu, Junxian Zhu, Xueqin Wang, 2025.
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Score-Based Diffusion Models in Function Space: Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar, 2025.
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Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms: Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira, 2025.
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WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings: Pablo Badilla, Felipe Bravo-Marquez, María José Zambrano, Jorge Pérez, 2025. (Machine Learning Open Source Software Paper)
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Frontiers to the learning of nonparametric hidden Markov models: Kweku Abraham, Elisabeth Gassiat, Zacharie Naulet, 2025.
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On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes: Zhiheng Chen, Guanhua Fang, Wen Yu, 2025.
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Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation: Stanislav Minsker, Mohamed Ndaoud, Yiqiu Shen, 2025.
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Universal Online Convex Optimization Meets Second-order Bounds: Lijun Zhang, Yibo Wang, Guanghui Wang, Jinfeng Yi, Tianbao Yang, 2025.
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Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens: Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard, 2025.
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Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests: Brian Liu, Rahul Mazumder, 2025.
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skglm: Improving scikit-learn for Regularized Generalized Linear Models: Badr Moufad, Pierre-Antoine Bannier, Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, 2025. (Machine Learning Open Source Software Paper)
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Losing Momentum in Continuous-time Stochastic Optimisation: Kexin Jin, Jonas Latz, Chenguang Liu, Alessandro Scagliotti, 2025.
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Latent Process Models for Functional Network Data: Peter W. MacDonald, Elizaveta Levina, Ji Zhu, 2025.
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Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models: Sudipto Banerjee, Xiang Chen, Ian Frankenburg, Daniel Zhou, 2025.
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On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory: Andrea Perin, Stephane Deny, 2025.
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Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems: Michal Dereziński, Daniel LeJeune, Deanna Needell, Elizaveta Rebrova, 2025.
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Deep Generative Models: Complexity, Dimensionality, and Approximation: Kevin Wang, Hongqian Niu, Yixin Wang, Didong Li, 2025.
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ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation: Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Christopher Bretherton, Veronika Eyring, Savannah Ferretti, Nicholas Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan M. Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael Pritchard, 2025.
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Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching: Jannis Chemseddine, Paul Hagemann, Gabriele Steidl, Christian Wald, 2025.
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Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses: Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini, 2025.
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Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space: Romain Verdière, Clémentine Prieur, Olivier Zahm, 2025.
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Finite Expression Method for Solving High-Dimensional Partial Differential Equations: Senwei Liang, Haizhao Yang, 2025.
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Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees: Ziwen Wang, Yancheng Yuan, Jiaming Ma, Tieyong Zeng, Defeng Sun, 2025.
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Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary: Tianyang Hu, Ruiqi Liu, Zuofeng Shang, Guang Cheng, 2025.
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Optimal and Efficient Algorithms for Decentralized Online Convex Optimization: Yuanyu Wan, Tong Wei, Bo Xue, Mingli Song, Lijun Zhang, 2025.
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Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning: Dennis Chemnitz, Maximilian Engel, 2025.
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PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks: Xiyue Zhang, Benjie Wang, Marta Kwiatkowska, Huan Zhang, 2025.
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Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability: Céline Comte, Matthieu Jonckheere, Jaron Sanders, Albert Senen-Cerda, 2025.
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On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm: Huan Li, Yiming Dong, Zhouchen Lin, 2025.
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Categorical Semantics of Compositional Reinforcement Learning: Georgios Bakirtzis, Michail Savvas, Ufuk Topcu, 2025.
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Transformers from Diffusion: A Unified Framework for Neural Message Passing: Qitian Wu, David Wipf, Junchi Yan, 2025.
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Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning: Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, Tong Zhang, 2025.
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Actor-Critic learning for mean-field control in continuous time: Noufel FRIKHA, Maximilien GERMAIN, Mathieu LAURIERE, Huyen PHAM, Xuanye SONG, 2025.
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Modelling Populations of Interaction Networks via Distance Metrics: George Bolt, Simón Lunagómez, Christopher Nemeth, 2025.
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BitNet: 1-bit Pre-training for Large Language Models: Hongyu Wang, Shuming Ma, Lingxiao Ma, Lei Wang, Wenhui Wang, Li Dong, Shaohan Huang, Huaijie Wang, Jilong Xue, Ruiping Wang, Yi Wu, Furu Wei, 2025.
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Physics-informed Kernel Learning: Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer, 2025.
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Last-iterate Convergence of Shuffling Momentum Gradient Method under the Kurdyka-Lojasiewicz Inequality: Yuqing Liang, Dongpo Xu, 2025.
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Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights: Paul Egels, Ismaël Castillo, 2025.
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Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL: Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi, 2025.
[abs][pdf][bib]

Degree of Interference: A General Framework For Causal Inference Under Interference: Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi, 2025.
[abs][pdf][bib]

Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo: Max Hird, Samuel Livingstone, 2025.
[abs][pdf][bib]

Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions: Penghe Zhang, Naihua Xiu, Hou-Duo Qi, 2025.
[abs][pdf][bib]

On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls: Anish Agarwal, Devavrat Shah, Dennis Shen, 2025.
[abs][pdf][bib] [code]

Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior: Ben Wu, Keru Wu, Jian Kang, 2025.
[abs][pdf][bib]

DRM Revisited: A Complete Error Analysis: Yuling Jiao, Ruoxuan Li, Peiying Wu, Jerry Zhijian Yang, Pingwen Zhang, 2025.
[abs][pdf][bib]

Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF: Han Shen, Zhuoran Yang, Tianyi Chen, 2025.
[abs][pdf][bib]

Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers: Fan Yang, Hongyang R. Zhang, Sen Wu, Christopher Re, Weijie J. Su, 2025.
[abs][pdf][bib] [code]

Score-based Causal Representation Learning: Linear and General Transformations: Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer, 2025.
[abs][pdf][bib] [code]

On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension: Saptarshi Chakraborty, Peter L. Bartlett, 2025.
[abs][pdf][bib]

Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms: Keru Wu, Yuansi Chen, Wooseok Ha, Bin Yu, 2025.
[abs][pdf][bib] [code]

Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles: Lesi Chen, Yaohua Ma, Jingzhao Zhang, 2025.
[abs][pdf][bib]

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos: Shao-Bo Lin, Xiaotong Liu, Di Wang, Hai Zhang, Ding-Xuan Zhou, 2025.
[abs][pdf][bib]

On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control: Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaid Harchaoui, 2025.
[abs][pdf][bib] [code]

A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds: Lei Wang, Le Bao, Xin Liu, 2025.
[abs][pdf][bib]

Learning conditional distributions on continuous spaces: Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal, 2025.
[abs][pdf][bib] [code]

A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs: Lukas Zierahn, Dirk van der Hoeven, Tal Lancewicki, Aviv Rosenberg, Nicolò Cesa-Bianchi, 2025.
[abs][pdf][bib] [code]

Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities: Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen, 2025.
[abs][pdf][bib]

Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors: Gaoyu Wu, Jelena Bradic, Kean Ming Tan, Wen-Xin Zhou, 2025.
[abs][pdf][bib]

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling: Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco, 2025.
[abs][pdf][bib] [code]

Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters: Florian Brück, Jean-David Fermanian, Aleksey Min, 2025.
[abs][pdf][bib] [code]

Statistical field theory for Markov decision processes under uncertainty: George Stamatescu, 2025.
[abs][pdf][bib]

Bayesian Data Sketching for Varying Coefficient Regression Models: Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee, 2025.
[abs][pdf][bib]

Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets: Hanyuan Hang, 2025.
[abs][pdf][bib]

Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals: David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas, 2025.
[abs][pdf][bib] [code]

Invariant Subspace Decomposition: Margherita Lazzaretto, Jonas Peters, Niklas Pfister, 2025.
[abs][pdf][bib] [code]

Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights: Insung Kong, Yongdai Kim, 2025.
[abs][pdf][bib]

Outlier Robust and Sparse Estimation of Linear Regression Coefficients: Takeyuki Sasai, Hironori Fujisawa, 2025.
[abs][pdf][bib]

Affine Rank Minimization via Asymptotic Log-Det Iteratively Reweighted Least Squares: Sebastian Krämer, 2025.
[abs][pdf][bib]

Causal Effect of Functional Treatment: Ruoxu Tan, Wei Huang, Zheng Zhang, Guosheng Yin, 2025.
[abs][pdf][bib] [code]

Uplift Model Evaluation with Ordinal Dominance Graphs: Brecht Verbeken, Marie-Anne Guerry, Wouter Verbeke, Sam Verboven, 2025.
[abs][pdf][bib]

High-Dimensional L2-Boosting: Rate of Convergence: Ye Luo, Martin Spindler, Jannis Kueck, 2025.
[abs][pdf][bib]

Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers: Federico Bassetti, Marco Gherardi, Alessandro Ingrosso, Mauro Pastore, Pietro Rotondo, 2025.
[abs][pdf][bib]

How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences: Mikolaj J. Kasprzak, Ryan Giordano, Tamara Broderick, 2025.
[abs][pdf][bib] [code]

Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test: Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani, 2025.
[abs][pdf][bib] [code]

On Inference for the Support Vector Machine: Jakub Rybak, Heather Battey, Wen-Xin Zhou, 2025.
[abs][pdf][bib]

Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis: Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang, 2025.
[abs][pdf][bib]

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability: Atticus Geiger, Duligur Ibeling, Amir Zur, Maheep Chaudhary, Sonakshi Chauhan, Jing Huang, Aryaman Arora, Zhengxuan Wu, Noah Goodman, Christopher Potts, Thomas Icard, 2025.
[abs][pdf][bib]

Implicit vs Unfolded Graph Neural Networks: Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf, 2025.
[abs][pdf][bib]

Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification: Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi, 2025.
[abs][pdf][bib]

GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia: Carlo Lucibello, Aurora Rossi, 2025. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Dynamic angular synchronization under smoothness constraints: Ernesto Araya, Mihai Cucuringu, Hemant Tyagi, 2025.
[abs][pdf][bib] [code]

Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems: Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas, 2025.
[abs][pdf][bib] [code]

Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds: Haoshu Xu, Hongzhe Li, 2025.
[abs][pdf][bib]

Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis: Youcheng Niu, Jinming Xu, Ying Sun, Yan Huang, Li Chai, 2025.
[abs][pdf][bib]

Learning causal graphs via nonlinear sufficient dimension reduction: Eftychia Solea, Bing Li, Kyongwon Kim, 2025.
[abs][pdf][bib]

On Consistent Bayesian Inference from Synthetic Data: Ossi Räisä, Joonas Jälkö, Antti Honkela, 2025.
[abs][pdf][bib] [code]

Optimization Over a Probability Simplex: James Chok, Geoffrey M. Vasil, 2025.
[abs][pdf][bib] [code]

Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method: Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton, Stanley Osher, 2025.
[abs][pdf][bib] [code]

Sampling and Estimation on Manifolds using the Langevin Diffusion: Karthik Bharath, Alexander Lewis, Akash Sharma, Michael V. Tretyakov, 2025.
[abs][pdf][bib]

Sharp Bounds for Sequential Federated Learning on Heterogeneous Data: Yipeng Li, Xinchen Lyu, 2025.
[abs][pdf][bib] [code]

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization: Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang, Zhiwei Bai, 2025.
[abs][pdf][bib]

Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy: Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao, 2025.
[abs][pdf][bib]

Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process: Feifei Wang, Zimeng Zhao, Ruimin Ye, Xiaoge Gu, Xiaoling Lu, 2025.
[abs][pdf][bib]

Deletion Robust Non-Monotone Submodular Maximization over Matroids: Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam, 2025.
[abs][pdf][bib]

Instability, Computational Efficiency and Statistical Accuracy: Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu, 2025.
[abs][pdf][bib]

Estimation of Local Geometric Structure on Manifolds from Noisy Data: Yariv Aizenbud, Barak Sober, 2025.
[abs][pdf][bib] [code]

Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python: Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Blübaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo, 2025. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Continuously evolving rewards in an open-ended environment: Richard M. Bailey, 2025.
[abs][pdf][bib]

Recursive Causal Discovery: Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash, 2025.
[abs][pdf][bib] [code]

Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings: Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi, 2025.
[abs][pdf][bib]

On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations: Antoine Godichon-Baggioni, Nicklas Werge, 2025.
[abs][pdf][bib]

Determine the Number of States in Hidden Markov Models via Marginal Likelihood: Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao, 2025.
[abs][pdf][bib]

Variance-Aware Estimation of Kernel Mean Embedding: Geoffrey Wolfer, Pierre Alquier, 2025.
[abs][pdf][bib]

Scaling ResNets in the Large-depth Regime: Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-Philippe Vert, 2025.
[abs][pdf][bib] [code]

A Comparative Evaluation of Quantification Methods: Tobias Schumacher, Markus Strohmaier, Florian Lemmerich, 2025.
[abs][pdf][bib] [code]

Lightning UQ Box: Uncertainty Quantification for Neural Networks: Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric Nalisnick, 2025. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Scaling Data-Constrained Language Models: Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel, 2025.
[abs][pdf][bib] [code]

Curvature-based Clustering on Graphs: Yu Tian, Zachary Lubberts, Melanie Weber, 2025.
[abs][pdf][bib]

Composite Goodness-of-fit Tests with Kernels: Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez, 2025.
[abs][pdf][bib] [code]

PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark: Jianqing Zhang, Yang Liu, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao, 2025. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning: Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu, 2025.
[abs][pdf][bib]

Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data: Pan Zhao, Julie Josse, Shu Yang, 2025.
[abs][pdf][bib] [code]

DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning: Xiangdong Xie, Jiahua Guo, Yi Sun, 2025.
[abs][pdf][bib] [code]

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization: Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng, 2025.
[abs][pdf][bib]

Manifold Fitting under Unbounded Noise: Zhigang Yao, Yuqing Xia, 2025.
[abs][pdf][bib]

Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play: Zelai Xu, Chao Yu, Yancheng Liang, Yi Wu, Yu Wang, 2025.
[abs][pdf][bib]

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models: Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu, 2025.
[abs][pdf][bib]

Extremal graphical modeling with latent variables via convex optimization: Sebastian Engelke, Armeen Taeb, 2025.
[abs][pdf][bib] [code]

On the Approximation of Kernel functions: Paul Dommel, Alois Pichler, 2025.
[abs][pdf][bib]

Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response: Jue Hou, Rajarshi Mukherjee, Tianxi Cai, 2025.
[abs][pdf][bib] [code]

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning: Kuangyu Ding, Jingyang Li, Kim-Chuan Toh, 2025.
[abs][pdf][bib]

Optimizing Data Collection for Machine Learning: Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law, 2025.
[abs][pdf][bib]

Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective: Shayan Hundrieser, Florian Heinemann, Marcel Klatt, Marina Struleva, Axel Munk, 2025.
[abs][pdf][bib]

Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding: Jiajing Zheng, Alexander D'Amour, Alexander Franks, 2025.
[abs][pdf][bib] [code]

Rank-one Convexification for Sparse Regression: Alper Atamturk, Andres Gomez, 2025.
[abs][pdf][bib]

gsplat: An Open-Source Library for Gaussian Splatting: Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, Angjoo Kanazawa, 2025. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming: Sen Na, Michael Mahoney, 2025.
[abs][pdf][bib]

Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds: Clément Bonet, Lucas Drumetz, Nicolas Courty, 2025.
[abs][pdf][bib] [code]

Accelerating optimization over the space of probability measures: Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright, 2025.
[abs][pdf][bib]

Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data: Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt, 2025.
[abs][pdf][bib] [code]

Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power: Jia He, Maggie Cheng, 2025.
[abs][pdf][bib]

Optimal Experiment Design for Causal Effect Identification: Sina Akbari, Jalal Etesami, Negar Kiyavash, 2025.
[abs][pdf][bib] [code]

Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data: Jie Peng, Weiyu Li, Stefan Vlaski, Qing Ling, 2025.
[abs][pdf][bib] [code]

The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond: Jiin Woo, Gauri Joshi, Yuejie Chi, 2025.
[abs][pdf][bib]

depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers: Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica, Mingsheng Long, 2025. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise: Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang, 2025.
[abs][pdf][bib]

Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick: Xiyuan Wang, Pan Li, Muhan Zhang, 2025.
[abs][pdf][bib] [code]

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables: Wei Jin, Yang Ni, Amanda B. Spence, Leah H. Rubin, Yanxun Xu, 2025.
[abs][pdf][bib] [code]

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions: Dapeng Yao, Fangzheng Xie, Yanxun Xu, 2025.
[abs][pdf][bib]

Regularizing Hard Examples Improves Adversarial Robustness: Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon, 2025.
[abs][pdf][bib]

Random ReLU Neural Networks as Non-Gaussian Processes: Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser, 2025.
[abs][pdf][bib]

Riemannian Bilevel Optimization: Jiaxiang Li, Shiqian Ma, 2025.
[abs][pdf][bib] [code]

Supervised Learning with Evolving Tasks and Performance Guarantees: Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano, 2025.
[abs][pdf][bib] [code]

Error estimation and adaptive tuning for unregularized robust M-estimator: Pierre C. Bellec, Takuya Koriyama, 2025.
[abs][pdf][bib]

From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective: Shaojun Guo, Dong Li, Xinghao Qiao, Yizhu Wang, 2025.
[abs][pdf][bib]

Locally Private Causal Inference for Randomized Experiments: Yuki Ohnishi, Jordan Awan, 2025.
[abs][pdf][bib]

Estimating Network-Mediated Causal Effects via Principal Components Network Regression: Alex Hayes, Mark M. Fredrickson, Keith Levin, 2025.
[abs][pdf][bib] [code]

Selective Inference with Distributed Data: Sifan Liu, Snigdha Panigrahi, 2025.
[abs][pdf][bib] [code]

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization: Tianyi Lin, Chi Jin, Michael I. Jordan, 2025.
[abs][pdf][bib]

An Axiomatic Definition of Hierarchical Clustering: Ery Arias-Castro, Elizabeth Coda, 2025.
[abs][pdf][bib]

Test-Time Training on Video Streams: Renhao Wang, Yu Sun, Arnuv Tandon, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang, 2025.
[abs][pdf][bib] [code]

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback: Boxin Zhao, Lingxiao Wang, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen, Mladen Kolar, 2025.
[abs][pdf][bib] [code]

A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation: Hugo Lebeau, Florent Chatelain, Romain Couillet, 2025.
[abs][pdf][bib]

Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents: Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss, 2025.
[abs][pdf][bib] [code]

Enhancing Graph Representation Learning with Localized Topological Features: Zuoyu Yan, Qi Zhao, Ze Ye, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen, 2025.
[abs][pdf][bib]

Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization: Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis, 2025.
[abs][pdf][bib]

DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data: Jiayi Tong, Jie Hu, George Hripcsak, Yang Ning, Yong Chen, 2025.
[abs][pdf][bib]

Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes: Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif, 2025.
[abs][pdf][bib]

Efficiently Escaping Saddle Points in Bilevel Optimization: Minhui Huang, Xuxing Chen, Kaiyi Ji, Shiqian Ma, Lifeng Lai, 2025.
[abs][pdf][bib]

Full list