JMLR

CVXPY: A Python-Embedded Modeling Language for Convex Optimization

Steven Diamond, Stephen Boyd

Comments Updated to JMLR version

Variational inference for Monte Carlo objectives

Andriy Mnih, Danilo J. Rezende

Comments Appears in Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, NY, USA, 2016. JMLR: W&CP volume 48

Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums

P. -L. Giscard, Z. Choo, S. J. Thwaite, D. Jaksch

Journal ref Journal of Machine Learning Research 17 (2016) 1-19

Evasion and Hardening of Tree Ensemble Classifiers

Alex Kantchelian, J. D. Tygar, Anthony D. Joseph

Comments 11 pages, 7 figures, Appears in Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, NY, USA, 2016. JMLR: W&CP volume 48

Mondrian Forests for Large-Scale Regression when Uncertainty Matters

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Comments Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 51

Hierarchical Compound Poisson Factorization

Mehmet E. Basbug, Barbara E. Engelhardt

Comments Will appear on Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48

Domain-Adversarial Training of Neural Networks

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky

Comments Published in JMLR: http://jmlr.org/papers/v17/15-239.html

Journal ref Journal of Machine Learning Research 2016, vol. 17, p. 1-35

One-Shot Generalization in Deep Generative Models

Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra

Comments 8pgs, 1pg references, 1pg appendix, In Proceedings of the 33rd International Conference on Machine Learning, JMLR: W&CP volume 48, 2016

Pseudo-Marginal Slice Sampling

Iain Murray, Matthew M. Graham

Comments 9 pages, 6 figures, 1 table. Version 2 includes citations to closely-related work released on arXiv since version 1

Journal ref Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, JMLR W&CP, Volume 51, pp. 911-919, 2016

Towards an Axiomatic Approach to Hierarchical Clustering of Measures

Philipp Thomann, Ingo Steinwart, Nico Schmid

Journal ref Journal of Machine Learning Research. 16(Sep):1949-2002, 2015

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions

Shusen Wang, Luo Luo, Zhihua Zhang

Comments Journal of Machine Learning Research, 2016

Recurrent Exponential-Family Harmoniums without Backprop-Through-Time

Joseph G. Makin, Benjamin K. Dichter, Philip N. Sabes

Comments 28 pages, 6 figures. Under review at JMLR since January 2016

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

Jure Žbontar, Yann LeCun

Journal ref JMLR 17(65):1-32, 2016

Variational Gaussian Copula Inference

Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin

Comments Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 51

Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

Sujith Ravi, Qiming Diao

Comments 10 pages

Journal ref Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP volume 51, pp. 519-528, 2016

The constrained Dantzig selector with enhanced consistency

Yinfei Kong, Zemin Zheng, Jinchi Lv

Comments to appear in Journal of Machine Learning Research

Distinguishing cause from effect using observational data: methods and benchmarks

Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf

Comments 101 pages, second revision submitted to Journal of Machine Learning Research

Journal ref Journal of Machine Learning Research 17(32):1-102, 2016

Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle

Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg

Comments to appear, Journal of Machine Learning Research, 2016

Consistent distribution-free $K$-sample and independence tests for univariate random variables

Ruth Heller, Yair Heller, Shachar Kaufman, Barak Brill, Malka Gorfine

Comments arXiv admin note: substantial text overlap with arXiv:1308.1559

Journal ref Journal of Machine Learning Research (JMLR) 2016, vol. 17, No. 29, 1-54

Random Intersection Trees

Rajen Dinesh Shah, Nicolai Meinshausen

Comments 23 pages, 4 figures

Journal ref Journal of Machine Learning Research 15 (2014) 629-654

Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks

Shiliang Zhang, Hui Jiang

Comments 31 pages, 5 Figures, technical report

Journal ref Journal of Machine Learning Research (JMLR), 17(37):1-33, 2016. (http://jmlr.org/papers/v17/15-335.html)

End-to-End Training of Deep Visuomotor Policies

Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel

Comments updating with revisions for JMLR final version

Sparse Activity and Sparse Connectivity in Supervised Learning

Markus Thom, Günther Palm

Comments See http://jmlr.org/papers/v14/thom13a.html for the authoritative version

Journal ref Journal of Machine Learning Research, vol. 14, pp. 1091-1143, 2013

Multiclass learnability and the ERM principle

Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz

Journal ref Journal of Machine Learning Research, 16(Jul):1275-1304, 2015

Loss minimization and parameter estimation with heavy tails

Daniel Hsu, Sivan Sabato

Comments Final version as published in JMLR

Journal ref Journal of Machine Learning Research, 17(18):1--40, 2016

Learning Sparse Low-Threshold Linear Classifiers

Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel Hsu, Tong Zhang

Journal ref Journal of Machine Learning Research, 16(Jul):1275-1304, 2015

Manifold unwrapping using density ridges

Jonas Nordhaug Myhre, Matineh Shaker, Devrim Kaba, Robert Jenssen, Deniz Erdogmus

Comments 43 pages, 29 figures, submitted to the Journal of Machine Learning Research

Hamiltonian Monte Carlo Without Detailed Balance

Jascha Sohl-Dickstein, Mayur Mudigonda, Michael R. DeWeese

Comments Accepted conference submission to ICML 2014 and also featured in a special edition of JMLR. Since updated to include additional literature citations

The Benefit of Multitask Representation Learning

Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes

Comments To appear in Journal of Machine Learning Research (JMLR). 31 pages

Photonic Delay Systems as Machine Learning Implementations

Michiel Hermans, Miguel Soriano, Joni Dambre, Peter Bienstman, Ingo Fischer

Journal ref Journal of Machine Learning Research, vol. 16, pp. 2081-2097 (2015)