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

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.
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Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization: Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu, 2026.
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Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood: Jiangrong Ouyang, Mingming Gong, Howard Bondell, 2026.
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A causal fused lasso for interpretable heterogeneous treatment effects estimation: Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla, Gabriel Ruiz, 2026.
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Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization: Yan Li, Defeng Sun, Liping Zhang, 2026.
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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.
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Hierarchical Causal Models: Eli N. Weinstein, David M. Blei, 2026.
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Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection: Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan, 2026.
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Adaptive Forward Stepwise: A Method for High Sparsity Regression: Ivy Zhang, Robert Tibshirani, 2026.
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Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width: Yunwen Lei, Puyu Wang, Yiming Ying, Ding-Xuan Zhou, 2026.
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Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection: Steven Adams, Andrea Patanè, Morteza Lahijanian, Luca Laurenti, 2026.
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CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration: Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret, 2026.
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Persistence Diagrams Estimation of Multivariate Piecewise Hölder-continuous Signals: Hugo Henneuse, 2026.
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Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling: Yudong Wang, Zhi-Sheng Ye, Cheng Yong Tang, 2026.
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Generative Bayesian Inference with GANs: Yuexi Wang, Veronika Rockova, 2026.
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Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information: Miaomiao Yu, Zhongfeng Jiang, Jiaxuan Li, Yong Zhou, 2026.
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Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models: Zijian Guo, Wei Yuan, Cunhui Zhang, 2026.
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Refined Risk Bounds for Unbounded Losses via Transductive Priors: Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy, 2026.
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A Common Interface for Automatic Differentiation: Guillaume Dalle, Adrian Hill, 2026. (Machine Learning Open Source Software Paper)
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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.
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The Distribution of Ridgeless Least Squares Interpolators: Qiyang Han, Xiaocong Xu, 2026.
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Nonparametric Estimation of a Factorizable Density using Diffusion Models: Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn, Minwoo Chae, 2026.
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Learning Bayesian Network Classifiers to Minimize Class Variable Parameters: Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno, 2026.
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Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation: Terrance D. Savitsky, Julie Gershunskaya, 2026.
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An Anytime Algorithm for Good Arm Identification: Marc Jourdan, Andrée Delahaye-Duriez, Clémence Réda, 2026.
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Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification: Aleksi Avela, Pauliina Ilmonen, 2026.
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Neural Network Parameter-optimization of Gaussian Pre-marginalized Directed Acyclic Graphs: Mehrzad Saremi, 2026. (Machine Learning Open Source Software Paper)
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Flexible Functional Treatment Effect Estimation: Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang, Kwun Chuen Gary Chan, 2026.
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Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence: Siming Zheng, Guohao Shen, Yuanyuan Lin, Jian Huang, 2026.
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A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design: Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, 2026.
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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)
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Nonlinear function-on-function regression by RKHS: Peijun Sang, Bing Li, 2026.
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Nonlocal Techniques for the Analysis of Deep ReLU Neural Network Approximations: Cornelia Schneider, Mario Ullrich, Jan Vybíral, 2026.
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A Data-Augmented Contrastive Learning Approach to Nonparametric Density Estimation: Chenghao Li, Yuanyuan Lin, 2026.
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Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent: Tong Wu, 2026.
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skwdro: a library for Wasserstein distributionally robust machine learning: Vincent Florian, Waïss Azizian, Franck Iutzeler, Jérôme Malick, 2026. (Machine Learning Open Source Software Paper)
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Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation: Bohan Wu, David M. Blei, 2026.
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Stochastic Gradient Methods: Bias, Stability and Generalization: Shuang Zeng, Yunwen Lei, 2026.
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Classification Under Local Differential Privacy with Model Reversal and Model Averaging: Caihong Qin, Yang Bai, 2026.
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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.
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Efficient frequent directions algorithms for approximate decomposition of matrices and higher-order tensors: Maolin Che, Yimin Wei, Hong Yan, 2026.
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Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios?: Ansgar Steland, 2026.
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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.
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Full list