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

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)
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Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming: Sen Na, Michael Mahoney, 2025.
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Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds: Clément Bonet, Lucas Drumetz, Nicolas Courty, 2025.
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Accelerating optimization over the space of probability measures: Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright, 2025.
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Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data: Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt, 2025.
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Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power: Jia He, Maggie Cheng, 2025.
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Optimal Experiment Design for Causal Effect Identification: Sina Akbari, Jalal Etesami, Negar Kiyavash, 2025.
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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.
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The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond: Jiin Woo, Gauri Joshi, Yuejie Chi, 2025.
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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)
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The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise: Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang, 2025.
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Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick: Xiyuan Wang, Pan Li, Muhan Zhang, 2025.
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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.
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Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions: Dapeng Yao, Fangzheng Xie, Yanxun Xu, 2025.
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Regularizing Hard Examples Improves Adversarial Robustness: Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon, 2025.
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Random ReLU Neural Networks as Non-Gaussian Processes: Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser, 2025.
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Riemannian Bilevel Optimization: Jiaxiang Li, Shiqian Ma, 2025.
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Supervised Learning with Evolving Tasks and Performance Guarantees: Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano, 2025.
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Error estimation and adaptive tuning for unregularized robust M-estimator: Pierre C. Bellec, Takuya Koriyama, 2025.
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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.
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Locally Private Causal Inference for Randomized Experiments: Yuki Ohnishi, Jordan Awan, 2025.
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Estimating Network-Mediated Causal Effects via Principal Components Network Regression: Alex Hayes, Mark M. Fredrickson, Keith Levin, 2025.
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Selective Inference with Distributed Data: Sifan Liu, Snigdha Panigrahi, 2025.
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Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization: Tianyi Lin, Chi Jin, Michael I. Jordan, 2025.
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An Axiomatic Definition of Hierarchical Clustering: Ery Arias-Castro, Elizabeth Coda, 2025.
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Test-Time Training on Video Streams: Renhao Wang, Yu Sun, Arnuv Tandon, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang, 2025.
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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.
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A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation: Hugo Lebeau, Florent Chatelain, Romain Couillet, 2025.
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Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents: Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss, 2025.
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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.
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Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization: Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis, 2025.
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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.
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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.
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Efficiently Escaping Saddle Points in Bilevel Optimization: Minhui Huang, Xuxing Chen, Kaiyi Ji, Shiqian Ma, Lifeng Lai, 2025.
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Full list