JMLR

NIMFA: A Python Library for Nonnegative Matrix Factorization

Marinka Zitnik, Blaz Zupan

Journal ref Journal of Machine Learning Research 13 (2012) 849-853

Learning Certifiably Optimal Rule Lists for Categorical Data

Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, Cynthia Rudin

Comments A short version of this work appeared in KDD '17 as "Learning Certifiably Optimal Rule Lists"

Journal ref Journal of Machine Learning Research 18(234):1-78, 2018

Characteristic and Universal Tensor Product Kernels

Zoltan Szabo, Bharath K. Sriperumbudur

Comments final version appeared in JMLR

Journal ref Journal of Machine Learning Research 18(233):1-29, 2018

Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

Comments 39 pages. Published in the Journal of Machine Learning Research (JMLR)

The DFS Fused Lasso: Linear-Time Denoising over General Graphs

Oscar Hernan Madrid Padilla, James G. Scott, James Sharpnack, Ryan J. Tibshirani

Journal ref Journal of Machine Learning Research, Vol. 18, No. 176, 1-36, 2018

Selective Clustering Annotated using Modes of Projections

Evan Greene, Greg Finak, Raphael Gottardo

Comments Submitted for journal review (JMLR) on July 21, 2018

Automatic differentiation in machine learning: a survey

Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind

Comments 43 pages, 5 figures

Journal ref Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind. Automatic differentiation in machine learning: a survey. The Journal of Machine Learning Research, 18(153):1--43, 2018

SGDLibrary: A MATLAB library for stochastic gradient descent algorithms

Hiroyuki Kasai

Journal ref Journal of Machine Learning Research, vol.18, no.215, pp.1-5, 2018

Sparse exchangeable graphs and their limits via graphon processes

Christian Borgs, Jennifer T. Chayes, Henry Cohn, Nina Holden

Comments 71 pages, 3 figures

Journal ref Journal of Machine Learning Research 18(210):1-71, 2018

Scalable Bayes via Barycenter in Wasserstein Space

Sanvesh Srivastava, Cheng Li, David B. Dunson

Comments 43 pages, 7 figures, and 11 tables. The updated revision will appear in JMLR

Enhancing Identification of Causal Effects by Pruning

Santtu Tikka, Juha Karvanen

Comments This is the version published in JMLR

Journal ref Journal of Machine Learning Research (JMLR), 18(194):1-23, 2018

Simplifying Probabilistic Expressions in Causal Inference

Santtu Tikka, Juha Karvanen

Comments This is the version published in JMLR

Journal ref Journal of Machine Learning Research (JMLR), 18(36):1-30, 2017

Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice

Hongzhou Lin, Julien Mairal, Zaid Harchaoui

Comments link to publisher website: http://jmlr.org/papers/volume18/17-748/17-748.pdf

Journal ref Journal of Machine Learning Research (JMLR), 18(212):1--54, 2018

Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

Quan Zhang, Mingyuan Zhou

Journal ref Journal of Machine Learning Research, vol. 18, pp. 1-33, 2018

Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs

Emilija Perković, Johannes Textor, Markus Kalisch, Marloes H. Maathuis

Comments 58 pages, 12 figures, to appear in JMLR

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

Comments Changes: - Updated to JMLR version

Journal ref Journal of Machine Learning Research 18 (2018) 1-52

Deterministic Stretchy Regression

Kar-Ann Toh, Lei Sun, Zhiping Lin

Comments Submitted for journal (JMLR) review since 28-Sept-2017

Scikit-learn: Machine Learning in Python

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay

Comments Update authors list and URLs

Journal ref Journal of Machine Learning Research (2011)

Maximum Principle Based Algorithms for Deep Learning

Qianxiao Li, Long Chen, Cheng Tai, Weinan E

Comments Published version

Journal ref Journal of Machine Learning Research 18 (2018) 1-29

Random Fourier Features for Operator-Valued Kernels

Romain Brault, Florence d'Alché-Buc, Markus Heinonen

Comments 32 pages, 6 figures

Journal ref ACML, Hamilton, New-Zealand, JMLR Workshop and Conference Proceedings, November 2016, vol. 63, pp. 110-125

Is feature selection secure against training data poisoning?

Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli

Journal ref Proc. of the 32nd ICML, Lille, France, 2015. JMLR: W&CP vol. 37

Inference Networks for Sequential Monte Carlo in Graphical Models

Brooks Paige, Frank Wood

Comments 10 pages. Updated from version at ICML 2016; includes code at http://github.com/tbrx/compiled-inference

Journal ref Paige, B., & Wood, F. (2016). Inference Networks for Sequential Monte Carlo in Graphical Models. In Proceedings of the 33rd International Conference on Machine Learning, JMLR W&CP 48: 3040-3049

Pyramid Person Matching Network for Person Re-identification

Chaojie Mao, Yingming Li, Zhongfei Zhang, Yaqing Zhang, Xi Li

Comments 11pages, 3 figures, 4 tables and accepted by Proceedings of 9th Asian Conference on Machine Learning (ACML2017) JMLR Workshop and Conference Proceedings, vol. 77, 2017

Kernel Embedding Approaches to Orbit Determination of Spacecraft Clusters

Srinagesh Sharma, James W. Cutler

Comments Submitted to JMLR

Steering Social Activity: A Stochastic Optimal Control Point Of View

Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez

Comments To appear in JMLR 2018. arXiv admin note: substantial text overlap with arXiv:1610.05773, arXiv:1703.02059

Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering

Pietro Coretto, Christian Hennig

Comments The title of this paper was originally: "A consistent and breakdown robust model-based clustering method"

Journal ref 2017, Journal of Machine Learning Research, Vol. 18(142), pp. 1-39. Download link: http://jmlr.org/papers/v18/16-382.html

BayesOpt: A Library for Bayesian optimization with Robotics Applications

Ruben Martinez-Cantin

Comments Robotics: Science and Systems, Workshop on Active Learning in Robotics: Exploration, Curiosity, and Interaction

Journal ref Journal of Machine Learning Research, 15(Nov), 3915-3919, 2014

Active-set Methods for Submodular Minimization Problems

K. S. Sesh Kumar, Francis Bach

Journal ref Journal of Machine Learning Research, Journal of Machine Learning Research, 2017, pp.1-31

Statistical inference on random dot product graphs: a survey

Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Youngser Park, Yichen Qin, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe

Comments An expository survey paper on a comprehensive paradigm for inference for random dot product graphs, centered on graph adjacency and Laplacian spectral embeddings. Paper outlines requisite background; summarizes theory, methodology, and applications from previous and ongoing work; and closes with a discussion of several open problems

Journal ref Journal of Machine Learning Research, 2018

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling

Christophe Dupuy, Francis Bach

Journal ref Journal of Machine Learning Research, Journal of Machine Learning Research, 2017, 18, pp.1 - 45