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.
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- Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
- Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou, 2020.
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- Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
- Yazhen Wang, Shang Wu, 2020.
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- Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
- Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu, 2020.
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- Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models
- Reza Mohammadi, Matthew Pratola, Maurits Kaptein, 2020.
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- A Numerical Measure of the Instability of Mapper-Type Algorithms
- Francisco Belchi, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan, 2020.
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- Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance
- Dalit Engelhardt, 2020.
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- Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
- Martin Slawski, Emanuel Ben-David, Ping Li, 2020.
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- Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms
- Simon Fischer, Ingo Steinwart, 2020.
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- On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
- Xiao-Tong Yuan, Ping Li, 2020.
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- Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging
- Baihua He, Yanyan Liu, Yuanshan Wu, Guosheng Yin, Xingqiu Zhao, 2020.
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- Learning Data-adaptive Non-parametric Kernels
- Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li, 2020.
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- 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.
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- 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)
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- Multiclass Anomaly Detector: the CS++ Support Vector Machine
- Alistair Shilton, Sutharshan Rajasegarar, Marimuthu Palaniswami, 2020.
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- Provable Convex Co-clustering of Tensors
- Eric C. Chi, Brian J. Gaines, Will Wei Sun, Hua Zhou, Jian Yang, 2020.
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- Mining Topological Structure in Graphs through Forest Representations
- Robin Vandaele, Yvan Saeys, Tijl De Bie, 2020.
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- Dynamic Assortment Optimization with Changing Contextual Information
- Xi Chen, Yining Wang, Yuan Zhou, 2020.
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