JMLR

Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots

Akshara Rai, Rika Antonova, Franziska Meier, Christopher G. Atkeson

Comments The first two authors made equal contributions

Journal ref Journal of Machine Learning Research (JMLR), 20(49):1-24, 2019

A Tight Bound of Hard Thresholding

Jie Shen, Ping Li

Comments V1 was submitted to COLT 2016. V2 fixes minor flaws, adds extra experiments and discusses time complexity, V3 has been accepted to JMLR

Journal ref Journal of Machine Learning Research 18(208): 1-42, 2018

Convergence of Sparse Variational Inference in Gaussian Processes Regression

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

Comments Extended version of http://proceedings.mlr.press/v97/burt19a.html (arxiv version: arXiv:1903.03571 ). Published in Journal of Machine Learning Research: http://jmlr.org/papers/v21/19-1015.html. Code available at: https://github.com/markvdw/RobustGP

Journal ref Journal of Machine Learning Research, 21(131), 1-63 (2020)

Unnormalized Variational Bayes

Saeed Saremi

Comments Submitted to Journal of Machine Learning Research

Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering

Israel Almodóvar-Rivera, Ranjan Maitra

Comments 32 pages, 22 figures, 9 tables: published in JMLR at: http://jmlr.org/papers/v21/18-435.html

Journal ref Journal of Machine Learning Research 21:122, 1-54 (2020)

metric-learn: Metric Learning Algorithms in Python

William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet

Comments GitHub repository: https://github.com/scikit-learn-contrib/metric-learn

Journal ref Journal of Machine Learning Research (JMLR), 21(138):1-6, 2020

Empirical priors for prediction in sparse high-dimensional linear regression

Ryan Martin, Yiqi Tang

Comments 29 pages, 1 figure, 7 tables. Comments welcome at https://www.researchers.one/article/2019-03-1

Journal ref Journal of Machine Learning Research, 2020, volume 21, pages 1--30

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information

T. Tony Cai, Tengyuan Liang, Alexander Rakhlin

Comments 31 pages, 1 figures

Journal ref Journal of Machine Learning Research 21 (2020) 1-34

A New Class of Time Dependent Latent Factor Models with Applications

Sinead A. Williamson, Michael Minyi Zhang, Paul Damien

Journal ref Journal of Machine Learning Research 21(27):1-24, 2020

Effective Ways to Build and Evaluate Individual Survival Distributions

Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner

Comments 34 pages (main text), 12 figures

Journal ref Journal of Machine Learning Research (JMLR) Volume 21 (2020) 18-772

Convex and Scalable Weakly Labeled SVMs

Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou

Journal ref Journal of Machine Learning Research, 2013, 14: 2151-2188

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R

Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

Journal ref Journal of Machine Learning Research 16 (2015) 553-557

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao

Journal ref Journal of Machine Learning Research 20 (2019): 44-1

The huge Package for High-dimensional Undirected Graph Estimation in R

Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman

Comments Published on JMLR in 2012

Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes

Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf

Journal ref Journal of Machine Learning Research 21 (2020) 1-53

Bayesian Model Selection with Graph Structured Sparsity

Youngseok Kim, Chao Gao

Journal ref Journal of Machine Learning Research 21(109):1-61, 2020

High-Dimensional Inference for Cluster-Based Graphical Models

Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu

Journal ref Journal of Machine Learning Research 21 (2020) 1-55

$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva

Journal ref Journal of Machine Learning Research. 2020;21(72):1-22

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright

Comments Version v3 consistent with paper appearing in JMLR

A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and $O(1)$ Constraint Violations for Online Convex Optimization with Long Term Constraints

Hao Yu, Michael J. Neely

Comments This paper is published in JMLR. The title is changed to emphasize that constraint violations attained by our algorithm is independent of the number of rounds $T$. In this version, we also analyze the regret and constraint violations for our algorithm without requiring the Slater condition

Scikit-Multiflow: A Multi-output Streaming Framework

Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem

Comments 5 pages, Open Source Software

Journal ref Journal of Machine Learning Research, 2019, vol. 1, p. 2915-2914

Expected Policy Gradients for Reinforcement Learning

Kamil Ciosek, Shimon Whiteson

Comments 36 pages, submitted for review to JMLR. This is an extended version of our paper in the AAAI-18 conference (arXiv:1706.05374)

Journal ref Journal of Machine Learning Research, Vol. 21, (52):1-51, 2020

A Convex Parametrization of a New Class of Universal Kernel Functions

Brendon K. Colbert, Matthew M. Peet

Comments 29 pages, 7 figures

Journal ref Journal of Machine Learning Research 21.45 (2020): 1-29

Representation Learning for Dynamic Graphs: A Survey

Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart

Comments Accepted at JMLR, 73 pages, 2 figures

Journal ref JMLR, Vol 21, Pages 1-73, 2020

Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery

Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei

Comments v2: fixed several typos; v3: accepted at JMLR

Journal ref Journal of Machine Learning Research 20 (114): 1-34, 2019

Neural Empirical Bayes

Saeed Saremi, Aapo Hyvarinen

Comments 23 pages, 10 figures

Journal ref Journal of Machine Learning Research 20(181), 1-23, 2019

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

Comments Journal of Machine Learning Research 2020, Special Issue on Bayesian Optimization

Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes

Anders Ellern Bilgrau, Carel F. W. Peeters, Poul Svante Eriksen, Martin Bøgsted, Wessel N. van Wieringen

Comments 52 pages, 11 figures

Journal ref Journal of Machine Learning Research, 21(26):1--52, 2020

Linear Latent Force Models using Gaussian Processes

Mauricio A. Álvarez, David Luengo, Neil D. Lawrence

Comments 20 pages, 2 figures. Extended technical report of the Conference Paper "Latent force models" in D. van Dyk and M. Welling (eds) Proceedings of the Twelfth International Workshop on Artificial Intelligence and Statistics, JMLR W&CP 5, Clearwater Beach, FL, pp 9--16

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables

Rune Christiansen, Jonas Peters

Comments 46 pages, 14 figures; real-world application added in Section 5.2; additional numerical experiments added in the Appendix E

Journal ref Journal of Machine Learning Research 21(41): 1--46, 2020