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