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

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

The d-Separation Criterion in Categorical Probability: Tobias Fritz, Andreas Klingler, 2023.
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The multimarginal optimal transport formulation of adversarial multiclass classification: Nicolás García Trillos, Matt Jacobs, Jakwang Kim, 2023.
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Robust Load Balancing with Machine Learned Advice: Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng, 2023.
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Benchmarking Graph Neural Networks: Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson, 2023.
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A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness: Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan, 2023.
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Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data: Shaowu Pan, Steven L. Brunton, J. Nathan Kutz, 2023.
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On Batch Teaching Without Collusion: Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles, 2023.
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Sensing Theorems for Unsupervised Learning in Linear Inverse Problems: Julián Tachella, Dongdong Chen, Mike Davies, 2023.
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First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems: Michael I. Jordan, Tianyi Lin, Manolis Zampetakis, 2023.
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Ridges, Neural Networks, and the Radon Transform: Michael Unser, 2023.
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Label Distribution Changing Learning with Sample Space Expanding: Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou, 2023.
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Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers?: Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan, 2023.
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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond: Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne, 2023. (Machine Learning Open Source Software Paper)
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Gap Minimization for Knowledge Sharing and Transfer: Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton, 2023.
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Sparse PCA: a Geometric Approach: Dimitris Bertsimas, Driss Lahlou Kitane, 2023.
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Labels, Information, and Computation: Efficient Learning Using Sufficient Labels: Shiyu Duan, Spencer Chang, Jose C. Principe, 2023.
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Attacks against Federated Learning Defense Systems and their Mitigation: Cody Lewis, Vijay Varadharajan, Nasimul Noman, 2023.
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HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn: Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard, 2023. (Machine Learning Open Source Software Paper)
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Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping: XuranMeng, JeffYao, 2023. (Machine Learning Open Source Software Paper)
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The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time: Raj Agrawal, Tamara Broderick, 2023.
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Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels: Hao Wang, Rui Gao, Flavio P. Calmon, 2023.
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Discrete Variational Calculus for Accelerated Optimization: Cédric M. Campos, Alejandro Mahillo, David Martín de Diego, 2023.
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Calibrated Multiple-Output Quantile Regression with Representation Learning: Shai Feldman, Stephen Bates, Yaniv Romano, 2023.
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Bayesian Data Selection: Eli N. Weinstein, Jeffrey W. Miller, 2023.
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Lower Bounds and Accelerated Algorithms for Bilevel Optimization: Kaiyi ji, Yingbin Liang, 2023.
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Graph-Aided Online Multi-Kernel Learning: Pouya M. Ghari, Yanning Shen, 2023.
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Interpolating Classifiers Make Few Mistakes: Tengyuan Liang, Benjamin Recht, 2023.
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Regularized Joint Mixture Models: Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee, 2023.
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An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization: Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis, 2023.
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Learning Mean-Field Games with Discounted and Average Costs: Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi, 2023.
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Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation: Cynthia Rudin, Yaron Shaposhnik, 2023.
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Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions: Jon Vadillo, Roberto Santana, Jose A. Lozano, 2023.
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Python package for causal discovery based on LiNGAM: Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu, 2023. (Machine Learning Open Source Software Paper)
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Adaptation to the Range in K-Armed Bandits: Hédi Hadiji, Gilles Stoltz, 2023.
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Learning-augmented count-min sketches via Bayesian nonparametrics: Emanuele Dolera, Stefano Favaro, Stefano Peluchetti, 2023.
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Optimal Strategies for Reject Option Classifiers: Vojtech Franc, Daniel Prusa, Vaclav Voracek, 2023.
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A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees: Michael J. O'Neill, Stephen J. Wright, 2023.
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Sampling random graph homomorphisms and applications to network data analysis: Hanbaek Lyu, Facundo Memoli, David Sivakoff, 2023.
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A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs: Radu I. Bot, Michael Sedlmayer, Phan Tu Vuong, 2023.
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On Distance and Kernel Measures of Conditional Dependence: Tianhong Sheng, Bharath K. Sriperumbudur, 2023.
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AutoKeras: An AutoML Library for Deep Learning: Haifeng Jin, François Chollet, Qingquan Song, Xia Hu, 2023. (Machine Learning Open Source Software Paper)
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Cluster-Specific Predictions with Multi-Task Gaussian Processes: Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey, 2023.
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Efficient Structure-preserving Support Tensor Train Machine: Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner, 2023.
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Bayesian Spiked Laplacian Graphs: Leo L Duan, George Michailidis, Mingzhou Ding, 2023.
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The Brier Score under Administrative Censoring: Problems and a Solution: Håvard Kvamme, Ørnulf Borgan, 2023.
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Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search: Benjamin Moseley, Joshua R. Wang, 2023.
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Full list