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

2026.03.02: Volume 26 completed; Volume 27 began.
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

Optimising Utility Functions in Multi-Objective Markov Decision Processes: Manel Rodriguez-Soto, 2026.
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Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach: Luca Presicce, Sudipto Banerjee, 2026.
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Symmetric Rank-k Methods: Chengchang Liu, Cheng chen, Luo Luo, 2026.
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From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis: Łukasz Dębowski, 2026.
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Efficient Inference under Label Shift in Unsupervised Domain Adaptation: Seong-ho Lee, Yanyuan Ma, Jiwei Zhao, 2026.
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Adaptive Algorithms for Infinitely Many-Armed Bandits: A Unified Framework: Emmanuel Pilliat, 2026.
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Gradient Estimation for Mixture Variational Inference: Javier Burroni, Daniel Sheldon, 2026.
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torchsom: The Reference PyTorch Library for Self-Organizing Maps: Louis Berthier, Ahmed Shokry, Maxime Moreaud, Guillaume Ramelet, Eric Moulines, 2026. (Machine Learning Open Source Software Paper)
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Pointwise Confidence Estimation in the Non-linear $\ell^2$-regularized Least Squares: Ilja Kuzborskij, Yasin Abbasi Yadkori, 2026.
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Safe Learning Under Irreversible Dynamics via Asking for Help: Benjamin Plaut, Juan Liévano-Karim, Hanlin Zhu, Stuart Russell, 2026.
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AgentPEN: A Prediction-Explanation Network for Sequential Stock Movement via LLMs and Recurrent Generation: Shuqi Li, Mengyao Guo, Yunzhong Zheng, Siqi Li, Xin Gao, Rui Yan, 2026.
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Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection: Lin Lu, Yuyang Huo, Haojie Ren, Zhaojun Wang, Changliang Zou, 2026.
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Ehrenfeucht-Haussler Rank and Chain of Thought: Pablo Barceló, Alexander Kozachinskiy, Tomasz Steifer, 2026.
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scikit-activeml: A Comprehensive and User-Friendly Active Learning Library: Marek Herde, Minh Tuan Pham, Daniel Kottke, Alexander Benz, Lukas Lührs, Pascal Mergard, Christoph Sandrock, Jiaying Cheng, Atal Roghman, Mehmet Müjde, Lukas Rauch, Bernhard Sick, 2026. (Machine Learning Open Source Software Paper)
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Locally Private Estimation with Public Features: Yuheng Ma, Hanfang Yang, Ke Jia, 2026.
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Dimension Reduction for Derivative-Informed Operator Learning: An Analysis of Approximation Errors: Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas, 2026.
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Domain Adaptation Targeting Heterogeneous and Imbalanced Subgroups: Doudou Zhou, Mengyan Li, Yun Wang, Tianxi Cai, Molei Liu, 2026.
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On the Effectiveness of the z-Transform Method in Quadratic Optimization: Francis Bach, 2026.
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Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions: Peng Wang, Huijie Zhang, Zekai Zhang, Siyi Chen, Yi Ma, Qing Qu, 2026.
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Consistency of Augmentation Graph and Network Approximability in Contrastive Learning: Chenghui Li, A. Martina Neuman, 2026.
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Solving Nonlinear PDEs with Sparse Radial Basis Function Networks: Zihan Shao, Konstantin Pieper, Xiaochuan Tian, 2026.
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Viscosity Convergence Analysis for Deep Q-Networks: Qian Qi, 2026.
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Clustering and Pruning in Causal Data Fusion: Otto Tabell, Santtu Tikka, Juha Karvanen, 2026.
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Leakage and Interpretability in Concept-Based Models: Enrico Parisini, Tapabrata Chakraborti, Chris Harbron, Ben D. MacArthur, Christopher R.S. Banerji, 2026.
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Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning: Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su, 2026.
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Incorporating external data for analyzing randomized clinical trials: A transfer learning approach: Yujia Gu, Hanzhong Liu, Wei Ma, 2026.
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A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization: Tianshu Chu, Dachuan Xu, Wei Yao, Chengming Yu, Jin Zhang, 2026.
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Particle Filter for Bayesian Inference on Privatized Data: Yu-Wei Chen, Pranav Sanghi, Jordan Awan, 2026.
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Test-time regression: a unifying framework for designing sequence models with associative memory: Ke Alexander Wang, Jiaxin Shi, Emily B. Fox, 2026.
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Nonparametric Spectral Density Estimation using Interactive Mechanisms under Local Differential Privacy: Cristina Butucea, Karolina Klockmann, Tatyana Krivobokova, 2026.
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Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters: Sebastian Salwig, Till Kahlke, Florian Hirschberger, Dennis Forster, Jörg Lücke, 2026.
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Statistical Inference for High-dimensional Partially Linear Models via Debiased Rank Lasso: Songshan Yang, Delin Zhao, Runze Li, 2026.
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Identifiability of the Instrumental Variable Model with the Treatment and Outcome Missing Not at Random: Shuozhi Zuo, Peng Ding, Fan Yang, 2026.
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Deep Neural Expected Shortfall Regression with Tail-Robustness: Myeonghun Yu, Kean Ming Tan, Huixia Judy Wang, Wen-Xin Zhou, 2026.
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Optimal Convergence Rates for Neural Operators: Mike Nguyen, Nicole Mücke, 2026.
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Have ASkotch: A Neat Solution for Large-Scale Kernel Ridge Regression: Pratik Rathore, Zachary Frangella, Jiaming Yang, Michał Dereziński, Madeleine Udell, 2026.
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Pairwise Comparisons without Stochastic Transitivity: Model, Theory and Applications: Sze Ming Lee, Yunxiao Chen, 2026.
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Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions: Claire Donnat, Elena Tuzhilina, 2026.
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Bayesian Level Set Clustering: David Buch, Miheer Dewaskar, David B. Dunson, 2026.
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Differentially Private Synthetic Data Generation for Relational Databases: Kaveh Alim, Hao Wang, Ojas Gulati, Akash Srivastava, Navid Azizan, 2026.
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A Neural Network Approach to Learning Solutions of a Class of Elliptic Variational Inequalities: Amal Alphonse, Michael Hintermüller, Alexander Kister, Chin Hang Lun, Clemens Sirotenko, 2026.
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Impatient Bandits: Optimizing for the Long-Term Without Delay: Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek, Lucas Maystre, Daniel Russo, 2026.
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Conditional Regression for the Nonlinear Single-Variable Model: Yantao Wu, Mauro Maggioni, 2026.
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Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models: Xichen Guo, Zheng Li, Biwei Huang, Yan Zeng, Zhi Geng, Feng Xie, 2026.
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Sliced Wasserstein Regression: Han Chen, Yidong Zhou, Hans-Georg Müller, 2026.
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Causal Falsification of Digital Twins: Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet, Chris Holmes, 2026.
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Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting: Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli, 2026.
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Extrapolation-Aware Nonparametric Statistical Inference: Niklas Pfister, Peter Bühlmann, 2026.
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Online Generalized Sparse Regression: How Does Overparametrization Help?: Shuoguang Yang, Qiang Sun, 2026.
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Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models: Dapeng Shi, Tiandong Wang, Zhiliang Ying, 2026.
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Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method: Bryan Andrews, Erich Kummerfeld, 2026.
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Singular-limit analysis of gradient descent with noise injection: Anna Shalova, André Schlichting, Mark Peletier, 2026.
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Information-Theoretic Safe Bayesian Optimization: Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters, 2026.
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Dirichlet Active Learning: Kevin Miller, Ryan Murray, 2026.
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Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Integrative Regression: Yue Liu, Molei Liu, Zijian Guo, Tianxi Cai, 2026.
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torchgfn: A PyTorch GFlowNet Library: Joseph D. Viviano, Omar G. Younis, Sanghyeok Choi, Victor Schmidt, Yoshua Bengio, Salem Lahlou, 2026. (Machine Learning Open Source Software Paper)
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Model-free generalized fiducial inference: Jonathan P Williams, 2026.
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Adversarial Rademacher Complexity of Deep Neural Networks: Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Zhi-Quan Luo, 2026.
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Keypoint-Guided Optimal Transport: Models, Algorithms, and Applications: Xiang Gu, Yucheng Yang, Wei Zeng, Jian Sun, Zongben Xu, 2026.
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The Role of Pseudo-Labels in Self-Training Linear Classifiers on High-Dimensional Gaussian Mixture Data: Takashi Takahashi, 2026.
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Bridging Domain Invariance and Diversity: A Fine-Grained Risk Bound for Domain Generalization: Xi Wang, Liang Bai, Xian Yang, Richard Yi Da Xu, Jiye Liang, 2026.
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High-Dimensional Analysis of Gradient Flow for Extensive-Width Quadratic Neural Networks: Simon Martin, Giulio Biroli, Francis Bach, 2026.
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Error Analyses of Auto-Regressive Video Diffusion Models: Jing Wang, Fengzhuo Zhang, Xiaoli Li, Vincent Y.~ F. Tan, Tianyu Pang, Chao Du, Aixin Sun, Zhuoran Yang, 2026.
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Near-optimal Delta-convex Estimation of Lipschitz Functions: Gábor Balázs, 2026.
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The Sample Complexity of Parameter-Free Stochastic Convex Optimization: Jared Lawrence, Ari Kalinsky, Hannah Bradfield, Yair Carmon, Oliver Hinder, 2026.
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End-to-End Deep Learning for Predicting Metric Space-Valued Outputs: Yidong Zhou, Su I Iao, Hans-Georg Müller, 2026.
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Graph-based Clustering Revisited: A Relaxation of Kernel k-Means Perspective: Wenlong Lyu, Yuheng Jia, Hui Liu, Junhui Hou, 2026.
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Learning to Play Two-Player Perfect-Information Games without Knowledge: Quentin Cohen-Solal, 2026.
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Doubly Debiased Robust Subsampling for Transfer Learning: Tao Wang, Weng Kee Wong, 2026.
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Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy: Philip Sosnin, Matthew Wicker, Josh Collyer, Calvin Tsay, 2026.
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Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression: Filippo Ascolani, Giacomo Zanella, 2026.
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Underdamped Langevin MCMC with third order convergence: Maximilian Scott, Dáire O'Kane, Andraž Jelinčič, James Foster, 2026.
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Approximation-Free Differentiable Oblique Decision Trees: Subrat Prasad Panda, Blaise Genest, Arvind Easwaran, 2026.
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Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes: Ruiqi Zhang, Jingfeng Wu, Licong Lin, Peter L. Bartlett, 2026.
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Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes: Bohan Wu, Eli N. Weinstein, Sohrab Salehi, Yixin Wang, David M. Blei, 2026.
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Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss: José Manuel de Frutos, Manuel A. Vázquez, Pablo M. Olmos, Joaquín Míguez, 2026.
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Gradient Span Algorithms Make Predictable Progress in High Dimension: Felix Benning, Leif Döring, 2026.
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py/cuTAGI: An Open-Source Library for Tractable Approximate Gaussian Inference in Bayesian Neural Networks: Luong-Ha Nguyen, James-A. Goulet, Miquel Florensa-Montilla, Van-Dai Vuong, 2026.
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Statistical Test for Attention in Transformers for Images and Time Series: Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Shuichi Nishino, Kouichi Taji, Ichiro Takeuchi, 2026.
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Accelerating Constrained Sampling: A Large Deviations Approach: Yingli Wang, Changwei Tu, Xiaoyu Wang, Lingjiong Zhu, 2026.
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Learning general conditional independence structures via the neighbourhood lattice: Arash A. Amini, Bryon Aragam, Qing Zhou, 2026.
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Statistical guarantees for denoising reflected diffusion models: Asbjørn Holk, Claudia Strauch, Lukas Trottner, 2026.
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Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes: Tim Gyger, Reinhard Furrer, Fabio Sigrist, 2026.
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STDE++: Polynomial-Time Amortization for Linear Differential Operators: Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi, 2026.
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The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler: Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe, Sifan Liu, 2026.
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Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation: Yuze Han, Xiang Li, Zhihua Zhang, 2026.
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Embedding Network Autoregression for Time Series Analysis and Causal Peer Effect Inference: Jae Ho Chang, Subhadeep Paul, 2026.
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Three Types of Calibration using Properties and their Semantic and Formal Relationships: Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson, 2026.
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Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex Optimization: Siyuan Zhang, Nachuan Xiao, Xin Liu, 2026.
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A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance: Axel F. Wolter, Tobias Sutter, 2026.
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FLAGG: Flexible Autoregressive Graph Generation: Samuel Cognolato, Alessandro Sperduti, Luciano Serafini, 2026.
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Nested Subspace Learning with Flags: Tom Szwagier, Xavier Pennec, 2026.
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A Unified Approach to Analysis and Design of Denoising Markov Models: Yinuo Ren, Grant M. Rotskoff, Lexing Ying, 2026.
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Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks: Nikolaos Tsilivis, Eitan Gronich, Julia Kempe, Gal Vardi, 2026.
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A Single-Loop Stochastic Proximal Quasi-Newton Method for Large-Scale Nonsmooth Convex Optimization: Yongcun Song, Zimeng Wang, Xiaoming Yuan, Hangrui Yue, 2026.
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Statistical Learning Theory for Neural Operators: Niklas Reinhardt, Sven Wang, Jakob Zech, 2026.
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Deconvolution in unlinked linear models: Fadoua Balabdaoui, Antonio Di Noia, Cécile Durot, 2026.
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Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines: Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda, Hachem Kadri, 2026.
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High-dimensional Parameter Transfer With Fused-Regularizer: Zelin He, Ying Sun, Jingyuan Liu, Runze Li, 2026.
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Exogenous Randomness Empowering Random Forests: Tianxing Mei, Yingying Fan, Jinchi Lv, 2026.
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Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data: Luoyao Yu, Lixing Zhu, Ruoqing Zhu, Xuehu Zhu, 2026.
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Deep Nonparametric Conditional Independence Tests for Images: Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven, 2026.
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Semi-supervised learning for linear extremile regression: Rong Jiang, Jiangfeng Wang, Keming Yu, 2026.
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Transfer Learning via Regularized Random-effects Linear Discriminant Analysis: Hongzhe Zhang, Arnab Auddy, Hongzhe Li, 2026.
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Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent: Henry Lam, Zitong Wang, 2026.
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A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equation: Shu Liu, Stanley Osher, Wuchen Li, 2026.
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Generalized Resubstitution for Regression Error Estimation: Diego Marcondes, Ulisses Braga-Neto, 2026.
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Transfer Conformal Predictive Inference for Regression: Ce Zhang, Ting Li, Jinhan Xie, Linglong Kong, Bei Jiang, 2026.
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Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions: Hongying Liu, Hao Wang, Haoran Chu, Yibo Wu, 2026.
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Node Regression on Latent Position Random Graphs via Local Averaging: Martin Gjorgjevski, Nicolas Keriven, Simon Barthelme, Yohann De Castro, 2026.
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On the Relevance of Byzantine Robust Optimization Against Data Poisoning: Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, 2026.
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Best Arm Identification with Minimal Regret: Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin, 2026.
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Convergence of Noise-Free Sampling Algorithms with Regularized Wasserstein Proximals: Fuqun Han, Stanley Osher, Wuchen Li, 2026.
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Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features: Jiuqi Wang, Shangtong Zhang, 2026.
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Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models: Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu, 2026.
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Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data: Wanli Hong, Shuyang Ling, 2026.
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Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control: Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang, 2026.
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Demographic Parity in Regression and Classification Within the Unawareness Framework: Vincent Divol, Solenne Gaucher, 2026.
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Enhancing Accuracy in Generative Models via Knowledge Transfer: Xinyu Tian, Xiaotong Shen, 2026.
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Multi-relational Network Autoregression Model with Latent Group Structures: Yimeng Ren, Xuening Zhu, Ganggang Xu, Yanyuan Ma, 2026.
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Limiting Over-Smoothing and Over-Squashing of Graph Message Passing by Deep Scattering Transforms: Yuanhong Jiang, Dongmian Zou, Xiaoqun Zhang, Yu Guang Wang, 2026.
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A Fully Parameter-Free Second-Order Algorithm for Convex-Concave Minimax Problems: Jun-Lin Wang, Zi Xu, Hui-Ling Zhang, 2026.
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Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent: Adrien Schertzer, Loucas Pillaud-Vivien, 2026.
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Vector-Valued Gaussian Processes for Approximating Divergence- or Rotation-free Vector Fields: Quoc Thong Le Gia, Ian Hugh Sloan, Holger Wendland, 2026.
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Differentially Private Best-Arm Identification: Achraf Azize, Marc Jourdan, Aymen Al Marjani, Debabrota Basu, 2026.
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Corruptions of Supervised Learning Problems: Typology and Mitigations: Laura Iacovissi, Nan Lu, Robert C. Williamson, 2026.
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Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies: Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien, 2026.
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A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization: Yuchen Zhu, Yufeng Zhang, Zhaoran Wang, Zhuoran Yang, Xiaohong Chen, 2026.
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Minimax density estimation in the adversarial framework under local differential privacy: Mélisande Albert, Juliette Chevallier, Béatrice Laurent, Ousmane Sacko, 2026.
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Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria: Ali D. Kara, Serdar Yüksel, 2026.
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Optimal Approximation and Generalization Errors for Deep Convolutional Neural Networks: Jinxin Wang, Shao-Bo Lin, 2026.
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Investigating the Histogram Loss in Regression: Ehsan Imani, Kai Luedemann, Sam Scholnick-Hughes, Esraa Elelimy, Martha White, 2026.
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Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing: Xianli Zeng, Kevin Jiang, Guang Cheng, Edgar Dobriban, 2026.
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Why "Classic" Transformers Are Shallow and A Depth-Enabling Technique: Yueyao Yu, Yin Zhang, 2026.
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Kernel-based Distributed Learning: Heng Lian, Xu Guo, 2026.
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A Convex Framework for Confounding Robust Inference: Kei Ishikawa, Niao He, Takafumi Kanamori, 2026.
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Global Fréchet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data: Álvaro Gajardo, Hans-Georg Müller, 2026.
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Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula: David Huk, Rilwan A. Adewoyin, Ritabrata Dutta, 2026.
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Sparse Topic Modeling via Spectral Decomposition and Thresholding: Huy Tran, Yating Liu, Claire Donnat, 2026.
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Nonparametric generative modeling for time series via Schrödinger bridge: Mohamed Hamdouche, Pierre Henry-Labordère, Huyên Pham, 2026.
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Do We Need to Penalize Variance of Losses for Learning with Label Noise?: Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu, 2026.
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Causal Influences over Social Learning Networks: Mert Kayaalp, Ali H. Sayed, 2026.
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Neural Exploitation and Exploration of Contextual Bandits: Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He, 2026.
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Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation: Luyang Fang, Haoran Lu, Yongkai Chen, Wenxuan Zhong, Ping Ma, 2026.
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Inference with non-differentiable surrogate loss in a general high-dimensional classification framework: Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao, 2026.
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A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks: Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna, 2026.
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The Role of Contextual Information in Best Arm Identification: Masahiro Kato, Kaito Ariu, 2026.
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Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective: Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan, 2026.
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Online Bernstein-von Mises theorem: Jeyong Lee, Junhyeok Choi, Minwoo Chae, 2026.
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Covariate-dependent Hierarchical Dirichlet Processes: Huizi Zhang, Sara Wade, Natalia Bochkina, 2026.
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DCatalyst: A Unified Accelerated Framework for Decentralized Optimization: TIanyu Cao, Xiaokai Chen, Gesualdo Scutari, 2026.
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Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models: Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister, 2026.
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Contrasting Local and Global Modeling with Machine Learning and Satellite Data: A Case Study Estimating Tree Canopy Height in African Savannas: Esther Rolf, Lucia Gordon, Milind Tambe, Andrew Davies, 2026.
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A Symplectic Analysis of Alternating Mirror Descent: Jonas E. Katona, Xiuyuan Wang, Andre Wibisono, 2026.
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Two-way Node Popularity Model for Directed and Bipartite Networks: Bing-Yi Jing, Ting Li, Jiangzhou Wang, Ya Wang, 2026.
[abs][pdf][bib] [code]

Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization: Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu, 2026.
[abs][pdf][bib]

Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood: Jiangrong Ouyang, Mingming Gong, Howard Bondell, 2026.
[abs][pdf][bib] [code]

A causal fused lasso for interpretable heterogeneous treatment effects estimation: Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla, Gabriel Ruiz, 2026.
[abs][pdf][bib]

Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization: Yan Li, Defeng Sun, Liping Zhang, 2026.
[abs][pdf][bib]

Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent: Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou, 2026.
[abs][pdf][bib]

Hierarchical Causal Models: Eli N. Weinstein, David M. Blei, 2026.
[abs][pdf][bib] [code]

Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection: Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan, 2026.
[abs][pdf][bib] [code]

Adaptive Forward Stepwise: A Method for High Sparsity Regression: Ivy Zhang, Robert Tibshirani, 2026.
[abs][pdf][bib]

Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width: Yunwen Lei, Puyu Wang, Yiming Ying, Ding-Xuan Zhou, 2026.
[abs][pdf][bib]

Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection: Steven Adams, Andrea Patanè, Morteza Lahijanian, Luca Laurenti, 2026.
[abs][pdf][bib]

CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration: Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret, 2026.
[abs][pdf][bib] [code]

Persistence Diagrams Estimation of Multivariate Piecewise Hölder-continuous Signals: Hugo Henneuse, 2026.
[abs][pdf][bib]

Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling: Yudong Wang, Zhi-Sheng Ye, Cheng Yong Tang, 2026.
[abs][pdf][bib]

Generative Bayesian Inference with GANs: Yuexi Wang, Veronika Rockova, 2026.
[abs][pdf][bib]

Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information: Miaomiao Yu, Zhongfeng Jiang, Jiaxuan Li, Yong Zhou, 2026.
[abs][pdf][bib]

Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models: Zijian Guo, Wei Yuan, Cunhui Zhang, 2026.
[abs][pdf][bib]

Refined Risk Bounds for Unbounded Losses via Transductive Priors: Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy, 2026.
[abs][pdf][bib]

A Common Interface for Automatic Differentiation: Guillaume Dalle, Adrian Hill, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport: Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, Omar Ghattas, 2026.
[abs][pdf][bib] [code]

The Distribution of Ridgeless Least Squares Interpolators: Qiyang Han, Xiaocong Xu, 2026.
[abs][pdf][bib]

Nonparametric Estimation of a Factorizable Density using Diffusion Models: Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn, Minwoo Chae, 2026.
[abs][pdf][bib]

Learning Bayesian Network Classifiers to Minimize Class Variable Parameters: Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno, 2026.
[abs][pdf][bib]

Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation: Terrance D. Savitsky, Julie Gershunskaya, 2026.
[abs][pdf][bib]

An Anytime Algorithm for Good Arm Identification: Marc Jourdan, Andrée Delahaye-Duriez, Clémence Réda, 2026.
[abs][pdf][bib]

Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification: Aleksi Avela, Pauliina Ilmonen, 2026.
[abs][pdf][bib] [code]

Neural Network Parameter-optimization of Gaussian Pre-marginalized Directed Acyclic Graphs: Mehrzad Saremi, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Flexible Functional Treatment Effect Estimation: Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang, Kwun Chuen Gary Chan, 2026.
[abs][pdf][bib]

Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence: Siming Zheng, Guohao Shen, Yuanyuan Lin, Jian Huang, 2026.
[abs][pdf][bib]

A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design: Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, 2026.
[abs][pdf][bib] [code]

UQLM: A Python Package for Uncertainty Quantification in Large Language Models: Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik, Ho-Kyeong Ra, Viren Bajaj, Zeya Ahmad, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Nonlinear function-on-function regression by RKHS: Peijun Sang, Bing Li, 2026.
[abs][pdf][bib]

Nonlocal Techniques for the Analysis of Deep ReLU Neural Network Approximations: Cornelia Schneider, Mario Ullrich, Jan Vybíral, 2026.
[abs][pdf][bib]

A Data-Augmented Contrastive Learning Approach to Nonparametric Density Estimation: Chenghao Li, Yuanyuan Lin, 2026.
[abs][pdf][bib] [code]

Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent: Tong Wu, 2026.
[abs][pdf][bib] [code]

skwdro: a library for Wasserstein distributionally robust machine learning: Vincent Florian, Waïss Azizian, Franck Iutzeler, Jérôme Malick, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation: Bohan Wu, David M. Blei, 2026.
[abs][pdf][bib]

Stochastic Gradient Methods: Bias, Stability and Generalization: Shuang Zeng, Yunwen Lei, 2026.
[abs][pdf][bib]

Classification Under Local Differential Privacy with Model Reversal and Model Averaging: Caihong Qin, Yang Bai, 2026.
[abs][pdf][bib]

Identifying Weight-Variant Latent Causal Models: Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi, 2026.
[abs][pdf][bib] [code]

Efficient frequent directions algorithms for approximate decomposition of matrices and higher-order tensors: Maolin Che, Yimin Wei, Hong Yan, 2026.
[abs][pdf][bib]

Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios?: Ansgar Steland, 2026.
[abs][pdf][bib]

The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks: Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs, 2026.
[abs][pdf][bib] [code]

Full list