JMLR

Persistence Images: A Stable Vector Representation of Persistent Homology

Henry Adams, Sofya Chepushtanova, Tegan Emerson, Eric Hanson, Michael Kirby, Francis Motta, Rachel Neville, Chris Peterson, Patrick Shipman, Lori Ziegelmeier

Comments Version 3 contains updated theoretical results supporting methodology; expanded discussion of related works; extended list of references; extended applications section; additional experimental results and new figures

Journal ref Journal of Machine Learning Research 18 (2017), Number 8, 1-35

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

Comments arXiv admin note: substantial text overlap with arXiv:1811.12359

Journal ref Journal of Machine Learning Research 2020, Volume 21, Number 209

Behavior Priors for Efficient Reinforcement Learning

Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

Comments Submitted to Journal of Machine Learning Research (JMLR)

Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance

Dalit Engelhardt

Journal ref Journal of Machine Learning Research 21(203): 1-30, 2020

Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models

Reza Mohammadi, Matthew Pratola, Maurits Kaptein

Comments Published at http://jmlr.org/papers/v21/19-307 in the Journal of Machine Learning Research (https://www.jmlr.org)

Journal ref Journal of Machine Learning Research 2020, Vol. 21, No. 201, 1-26

Quadratic Decomposable Submodular Function Minimization: Theory and Practice (Computation and Analysis of PageRank over Hypergraphs)

Pan Li, Niao He, Olgica Milenkovic

Comments A part of the work appeared in NeurIPS 2018. The current version is to appear in JMLR (Revise some typos). arXiv admin note: substantial text overlap with arXiv:1806.09842

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

Comments 52 pages, 11 figures; Preliminary version in International Conference on Machine Learning 2018

Journal ref Journal of Machine Learning Research 21 (2020) 1-54

Learning Optimized Risk Scores

Berk Ustun, Cynthia Rudin

Journal ref Journal of Machine Learning Research 2019. Volume 20. Issue 150. Pages 1-75

Learning Data-adaptive Nonparametric Kernels

Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li

Comments This paper was accepted by Journal of Machine Learning Research (JMLR)

Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection

Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen, César L. C. Mattos, Amauri H. Souza Júnior, João P. P. Gomes

Comments 29 pages, Accepted to JMLR

Trust-Region Variational Inference with Gaussian Mixture Models

Oleg Arenz, Mingjun Zhong, Gerhard Neumann

Journal ref Journal of Machine Learning Research. 21(163):1-60, 2020

Learning and Interpreting Multi-Multi-Instance Learning Networks

Alessandro Tibo, Manfred Jaeger, Paolo Frasconi

Comments JMLR

Monte Carlo Gradient Estimation in Machine Learning

Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

Comments 62 pages

Journal ref Journal of Machine Learning Research, 21(132):1-62, 2020

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara

Journal ref Journal of Machine Learning Research, 21(172):1-29, 2020 (http://jmlr.org/papers/v21/19-993.html)

Learning big Gaussian Bayesian networks: partition, estimation, and fusion

Jiaying Gu, Qing Zhou

Comments 26 pages, 2 figures, and 5 tables

Journal ref Journal of Machine Learning Research, 21(158): 1-31, 2020

Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes

Emily C. Hector, Peter X. -K. Song

Comments 49 pages, 1 figure

Journal ref Journal of Machine Learning Research, 21(173):1-35, 2020

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson

Comments Extended version of the ICML 2018 conference paper (arXiv:1803.11485)

Journal ref Journal of Machine Learning Research 21(178):1-51, 2020

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality

Miaoyan Wang, Lexin Li

Comments 35 pages, 7 figures, 4 tables

Journal ref Journal of Machine Learning Research, 21(154): 1-38, 2020

New insights and perspectives on the natural gradient method

James Martens

Comments Minor corrections from previous version and fixed typos. Official JMLR version

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone

Journal ref Journal of Machine Learning Research 21(181):1-50, 2020

Cramer-Wold AutoEncoder

Szymon Knop, Jacek Tabor, Przemysław Spurek, Igor Podolak, Marcin Mazur, Stanisław Jastrzębski

Journal ref Journal of Machine Learning Research, 21, 164, 1-28 2020

Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality

Ryumei Nakada, Masaaki Imaizumi

Comments 38 pages

Journal ref Journal of Machine Learning Research, 21(174), 2020

Probabilistic symmetries and invariant neural networks

Benjamin Bloem-Reddy, Yee Whye Teh

Comments Revised structure for clarity; fixed minor mistakes; incorporated reviewer feedback for publication

Journal ref Journal of Machine Learning Research, 21(90):1-61, 2020

Scikit-network: Graph Analysis in Python

Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier

Journal ref Journal of Machine Learning Research, Microtome Publishing, In press

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success

Lucas Mentch, Siyu Zhou

Comments To Appear in the Journal of Machine Learning Research (JMLR)

Contextual Explanation Networks

Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing

Comments 48 pages, 18 figures, to appear in JMLR

Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise

Andrei Kulunchakov, Julien Mairal

Comments Journal of Machine Learning Research, Microtome Publishing, In press

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

James Townsend, Niklas Koep, Sebastian Weichwald

Journal ref Journal of Machine Learning Research, 17(137):1-5, 2016 ( https://jmlr.org/papers/v17/16-177.html )

Learning All Credible Bayesian Network Structures for Model Averaging

Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek

Comments under review by JMLR. arXiv admin note: substantial text overlap with arXiv:1811.05039

Joint Causal Inference from Multiple Contexts

Joris M. Mooij, Sara Magliacane, Tom Claassen

Comments Final version, as published by JMLR

Journal ref Journal of Machine Learning Research 21(99):1-108, 2020