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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- 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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- 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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- Lower Bounds and Accelerated Algorithms for Bilevel Optimization
- Kaiyi ji, Yingbin Liang, 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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- 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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