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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.
[abs][pdf][bib]

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

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

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

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

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.
[abs][pdf][bib]

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

Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms
Simon Fischer, Ingo Steinwart, 2020.
[abs][pdf][bib]

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

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

Learning Data-adaptive Non-parametric Kernels
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li, 2020.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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

Mining Topological Structure in Graphs through Forest Representations
Robin Vandaele, Yvan Saeys, Tijl De Bie, 2020.
[abs][pdf][bib]

Dynamic Assortment Optimization with Changing Contextual Information
Xi Chen, Yining Wang, Yuan Zhou, 2020.
[abs][pdf][bib]

Full list

© JMLR 2020.