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Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR) 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

A determinantal point process for column subset selection
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais, 2020.

Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou, 2020.

Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
Yazhen Wang, Shang Wu, 2020.

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu, 2020.

Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models
Reza Mohammadi, Matthew Pratola, Maurits Kaptein, 2020.

A Numerical Measure of the Instability of Mapper-Type Algorithms
Francisco Belchi, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan, 2020.
[abs][pdf][bib]      [code]

Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance
Dalit Engelhardt, 2020.

Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
Martin Slawski, Emanuel Ben-David, Ping Li, 2020.

Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms
Simon Fischer, Ingo Steinwart, 2020.

On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
Xiao-Tong Yuan, Ping Li, 2020.

Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging
Baihua He, Yanyan Liu, Yuanshan Wu, Guosheng Yin, Xingqiu Zhao, 2020.

Learning Data-adaptive Non-parametric Kernels
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li, 2020.

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem, 2020.
[abs][pdf][bib]      [code]

ProtoAttend: Attention-Based Prototypical Learning
Sercan O. Arik, Tomas Pfister, 2020.

Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang, 2020.
[abs][pdf][bib]      [code]

scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
Sebastian Pölsterl, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Multiclass Anomaly Detector: the CS++ Support Vector Machine
Alistair Shilton, Sutharshan Rajasegarar, Marimuthu Palaniswami, 2020.

Provable Convex Co-clustering of Tensors
Eric C. Chi, Brian J. Gaines, Will Wei Sun, Hua Zhou, Jian Yang, 2020.

Mining Topological Structure in Graphs through Forest Representations
Robin Vandaele, Yvan Saeys, Tijl De Bie, 2020.

Dynamic Assortment Optimization with Changing Contextual Information
Xi Chen, Yining Wang, Yuan Zhou, 2020.

Full list

© JMLR 2020.