JMLR

DESlib: A Dynamic ensemble selection library in Python

Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti

Comments Paper introducing DESlib: A dynamic ensemble selection library in Python

Journal ref Journal of Machine Learning Research, 21 (2020), 1-5

Embarrassingly Parallel Inference for Gaussian Processes

Michael Minyi Zhang, Sinead A. Williamson

Journal ref Journal of Machine Learning Research 20, no. 169 (2019): 1-26

Supervised Dimensionality Reduction and Visualization using Centroid-encoder

Tomojit Ghosh, Michael Kirby

Comments 25 pages (including 3 reference pages), 12 figures. I am planning to submit the paper to JMLR very soon. Centroid-encoder was applied on a biological pathway data (https://www.sciencedirect.com/science/article/pii/S1046202317300439). In this paper we throughly analyzed the algorithm and compared it with state-of-the art techniques on a 8 data sets including MNIST, USPS

Learning with Fenchel-Young Losses

Mathieu Blondel, André F. T. Martins, Vlad Niculae

Comments In Journal of Machine Learning Research, volume 21

Dynamical Systems as Temporal Feature Spaces

Peter Tino

Comments 45 pages, 17 figures, accepted

Journal ref JMLR, 2020

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu

Journal ref Journal of Machine Learning Research 21 (2020) 1-7

Why do deep convolutional networks generalize so poorly to small image transformations?

Aharon Azulay, Yair Weiss

Journal ref JMLR 20(184) 1-25 2019

MushroomRL: Simplifying Reinforcement Learning Research

Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

Comments Under revision to JMLR

Determinantal Point Processes for Coresets

Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard

Journal ref Journal of Machine Learning Research 20 (2019) 1-70

The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks

Arjun Sondhi, Ali Shojaie

Journal ref Journal of Machine Learning Research, 20(164), 1-31 (2019)

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Aaron Fisher, Cynthia Rudin, Francesca Dominici

Comments The final, published article is now available at http://www.jmlr.org/papers/v20/18-760.html . This version contains minor changes to the introductory sections, and some typo corrections. The title of this article changed twice during the revision process (see v1 and v3 on arxiv)

Journal ref Journal of Machine Learning Research 20 (177), 1-81, 2019

Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions

Carl-Johann Simon-Gabriel, Bernhard Schölkopf

Comments Old and longer version of the JMLR paper with same title (published 2018). Please start with the JMLR version. 55 pages (33 pages main text, 22 pages appendix), 2 tables, 1 figure (in appendix)

Journal ref Journal of Machine Learning Research, 19(44):1-29, 2018

Towards Ultrahigh Dimensional Feature Selection for Big Data

Mingkui Tan, Ivor W. Tsang, Li Wang

Comments 61 pages

Journal ref JMLR, 15(1): 1371 - 1429, 2014

Log-concave sampling: Metropolis-Hastings algorithms are fast

Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu

Comments 42 pages, 11 Figures; The first two authors contributed equally; A subset of results were presented in an extended abstract at COLT 2018

Journal ref Journal of Machine Learning Research, 2019

Joint PLDA for Simultaneous Modeling of Two Factors

Luciana Ferrer, Mitchell McLaren

Comments Submitted to Journal of Machine Learning Research

Journal ref Journal of Machine Learning Research, January, 2019

Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence Modeling

Satyajit Neogi, Justin Dauwels

Comments To be submitted to Journal of Machine Learning Research (JMLR)

New Convergence Aspects of Stochastic Gradient Algorithms

Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takáč, Marten van Dijk

Comments Journal of Machine Learning Research. arXiv admin note: substantial text overlap with arXiv:1802.03801

Dual Principal Component Pursuit

Manolis C. Tsakiris, Rene Vidal

Comments fixed two typos in section 7.3

Journal ref Journal of Machine Learning Research 19 (2018) 1-49

Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes

Peter D. Grünwald, Nishant A. Mehta

Comments accepted to JMLR pending minor final modifications

Pattern Search Multidimensional Scaling

Georgios Paraskevopoulos, Efthymios Tzinis, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Alexandros Potamianos

Comments 36 pages, Under review for JMLR

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

Comments equal contribution between Pfister and Weichwald

Journal ref Journal of Machine Learning Research, 20(147):1-50, 2019. ( http://www.jmlr.org/papers/v20/18-399.html )

GraSPy: Graph Statistics in Python

Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein

Journal ref Journal of Machine Learning Research 20.158 (2019): 1-7

Finite-time optimality of Bayesian predictors

Daniil Ryabko

Journal ref On Asymptotic and Finite-Time Optimality of Bayesian Predictors, Journal of Machine Learning Research (149):1-24, 2019

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker

Comments The published version of this article can be found at http://jmlr.org/papers/v20/19-033.html

Journal ref Journal of Machine Learning Research 20 (2019)

Generalized maximum entropy estimation

Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros

Comments 29 pages, 3 figures; v2: approximate dynamic programming section added, v3: published version

Journal ref Journal of Machine Learning Research, vol 20, 2019

Stochastic Canonical Correlation Analysis

Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang

Comments Accepted by JMLR

A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

Tyler Maunu, Teng Zhang, Gilad Lerman

Comments 58 pages, 6 figures, 1 table

Journal ref Journal of Machine Learning Research, 20(37):1-59, 2019

Causal Learning via Manifold Regularization

Steven M. Hill, Chris. J. Oates, Duncan A. Blythe, Sach Mukherjee

Journal ref Journal of Machine Learning Research 20(127):1-32, 2019

Hidden Talents of the Variational Autoencoder

Bin Dai, Yu Wang, John Aston, Gang Hua, David Wipf

Journal ref The Journal of Machine Learning Research, Volume 19 Issue 1, January 2018 Pages 1573-1614

Distributed Inference for Linear Support Vector Machine

Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu

Comments 50 pages, 11 figures

Journal ref Journal of Machine Learning Research (JMLR), v20(113):1-41, 2019