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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.

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Latest papers

The d-Separation Criterion in Categorical Probability
Tobias Fritz, Andreas Klingler, 2023.
[abs][pdf][bib]

The multimarginal optimal transport formulation of adversarial multiclass classification
Nicolás García Trillos, Matt Jacobs, Jakwang Kim, 2023.
[abs][pdf][bib]

Robust Load Balancing with Machine Learned Advice
Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng, 2023.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
Julián Tachella, Dongdong Chen, Mike Davies, 2023.
[abs][pdf][bib]

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

Ridges, Neural Networks, and the Radon Transform
Michael Unser, 2023.
[abs][pdf][bib]

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

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
Shiyu Duan, Spencer Chang, Jose C. Principe, 2023.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Learning-augmented count-min sketches via Bayesian nonparametrics
Emanuele Dolera, Stefano Favaro, Stefano Peluchetti, 2023.
[abs][pdf][bib]

Optimal Strategies for Reject Option Classifiers
Vojtech Franc, Daniel Prusa, Vaclav Voracek, 2023.
[abs][pdf][bib]

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

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.
[abs][pdf][bib]

On Distance and Kernel Measures of Conditional Dependence
Tianhong Sheng, Bharath K. Sriperumbudur, 2023.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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

Full list

© JMLR 2023.
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