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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.


Latest papers

The d-Separation Criterion in Categorical Probability
Tobias Fritz, Andreas Klingler, 2023.

The multimarginal optimal transport formulation of adversarial multiclass classification
Nicolás García Trillos, Matt Jacobs, Jakwang Kim, 2023.

Robust Load Balancing with Machine Learned Advice
Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng, 2023.

Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson, 2023.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
Shaowu Pan, Steven L. Brunton, J. Nathan Kutz, 2023.
[abs][pdf][bib]      [code]

On Batch Teaching Without Collusion
Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles, 2023.

Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
Julián Tachella, Dongdong Chen, Mike Davies, 2023.

First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis, 2023.

Ridges, Neural Networks, and the Radon Transform
Michael Unser, 2023.

Label Distribution Changing Learning with Sample Space Expanding
Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou, 2023.

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.

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)
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Sparse PCA: a Geometric Approach
Dimitris Bertsimas, Driss Lahlou Kitane, 2023.

Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
Shiyu Duan, Spencer Chang, Jose C. Principe, 2023.

Attacks against Federated Learning Defense Systems and their Mitigation
Cody Lewis, Vijay Varadharajan, Nasimul Noman, 2023.
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
Raj Agrawal, Tamara Broderick, 2023.

Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
Hao Wang, Rui Gao, Flavio P. Calmon, 2023.

Discrete Variational Calculus for Accelerated Optimization
Cédric M. Campos, Alejandro Mahillo, David Martín de Diego, 2023.
[abs][pdf][bib]      [code]

Calibrated Multiple-Output Quantile Regression with Representation Learning
Shai Feldman, Stephen Bates, Yaniv Romano, 2023.
[abs][pdf][bib]      [code]

Bayesian Data Selection
Eli N. Weinstein, Jeffrey W. Miller, 2023.
[abs][pdf][bib]      [code]

Lower Bounds and Accelerated Algorithms for Bilevel Optimization
Kaiyi ji, Yingbin Liang, 2023.

Graph-Aided Online Multi-Kernel Learning
Pouya M. Ghari, Yanning Shen, 2023.
[abs][pdf][bib]      [code]

Interpolating Classifiers Make Few Mistakes
Tengyuan Liang, Benjamin Recht, 2023.

Regularized Joint Mixture Models
Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee, 2023.
[abs][pdf][bib]      [code]

An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization
Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis, 2023.
[abs][pdf][bib]      [code]

Learning Mean-Field Games with Discounted and Average Costs
Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi, 2023.

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
Cynthia Rudin, Yaron Shaposhnik, 2023.
[abs][pdf][bib]      [code]

Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Jon Vadillo, Roberto Santana, Jose A. Lozano, 2023.
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

Adaptation to the Range in K-Armed Bandits
Hédi Hadiji, Gilles Stoltz, 2023.

Learning-augmented count-min sketches via Bayesian nonparametrics
Emanuele Dolera, Stefano Favaro, Stefano Peluchetti, 2023.

Optimal Strategies for Reject Option Classifiers
Vojtech Franc, Daniel Prusa, Vaclav Voracek, 2023.

A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees
Michael J. O'Neill, Stephen J. Wright, 2023.

Sampling random graph homomorphisms and applications to network data analysis
Hanbaek Lyu, Facundo Memoli, David Sivakoff, 2023.
[abs][pdf][bib]      [code]

A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs
Radu I. Bot, Michael Sedlmayer, Phan Tu Vuong, 2023.

On Distance and Kernel Measures of Conditional Dependence
Tianhong Sheng, Bharath K. Sriperumbudur, 2023.

AutoKeras: An AutoML Library for Deep Learning
Haifeng Jin, François Chollet, Qingquan Song, Xia Hu, 2023. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Cluster-Specific Predictions with Multi-Task Gaussian Processes
Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey, 2023.
[abs][pdf][bib]      [code]

Efficient Structure-preserving Support Tensor Train Machine
Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner, 2023.
[abs][pdf][bib]      [code]

Bayesian Spiked Laplacian Graphs
Leo L Duan, George Michailidis, Mingzhou Ding, 2023.
[abs][pdf][bib]      [code]

The Brier Score under Administrative Censoring: Problems and a Solution
Håvard Kvamme, Ørnulf Borgan, 2023.

Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search
Benjamin Moseley, Joshua R. Wang, 2023.

Full list

© JMLR 2023.