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

Estimation of Local Geometric Structure on Manifolds from Noisy Data: Yariv Aizenbud, Barak Sober, 2025.
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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)
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Continuously evolving rewards in an open-ended environment: Richard M. Bailey, 2025.
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Recursive Causal Discovery: Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash, 2025.
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Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings: Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi, 2025.
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On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations: Antoine Godichon-Baggioni, Nicklas Werge, 2025.
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Determine the Number of States in Hidden Markov Models via Marginal Likelihood: Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao, 2025.
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Variance-Aware Estimation of Kernel Mean Embedding: Geoffrey Wolfer, Pierre Alquier, 2025.
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Scaling ResNets in the Large-depth Regime: Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-Philippe Vert, 2025.
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A Comparative Evaluation of Quantification Methods: Tobias Schumacher, Markus Strohmaier, Florian Lemmerich, 2025.
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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)
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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.
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Curvature-based Clustering on Graphs: Yu Tian, Zachary Lubberts, Melanie Weber, 2025.
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Composite Goodness-of-fit Tests with Kernels: Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez, 2025.
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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)
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The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning: Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu, 2025.
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Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data: Pan Zhao, Julie Josse, Shu Yang, 2025.
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DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning: Xiangdong Xie, Jiahua Guo, Yi Sun, 2025.
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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.
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Manifold Fitting under Unbounded Noise: Zhigang Yao, Yuqing Xia, 2025.
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Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play: Zelai Xu, Chao Yu, Yancheng Liang, Yi Wu, Yu Wang, 2025.
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Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models: Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu, 2025.
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Extremal graphical modeling with latent variables via convex optimization: Sebastian Engelke, Armeen Taeb, 2025.
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On the Approximation of Kernel functions: Paul Dommel, Alois Pichler, 2025.
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Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response: Jue Hou, Rajarshi Mukherjee, Tianxi Cai, 2025.
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Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning: Kuangyu Ding, Jingyang Li, Kim-Chuan Toh, 2025.
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Optimizing Data Collection for Machine Learning: Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law, 2025.
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Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective: Shayan Hundrieser, Florian Heinemann, Marcel Klatt, Marina Struleva, Axel Munk, 2025.
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Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding: Jiajing Zheng, Alexander D'Amour, Alexander Franks, 2025.
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Rank-one Convexification for Sparse Regression: Alper Atamturk, Andres Gomez, 2025.
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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