JMLR

On the Complexity of Best Arm Identification in Multi-Armed Bandit Models

Emilie Kaufmann, Olivier Cappé, Aurélien Garivier

Comments arXiv admin note: text overlap with arXiv:1405.3224

Journal ref Journal of Machine Learning Research, Journal of Machine Learning Research, 2016, 17, pp.1-42

Operator-valued Kernels for Learning from Functional Response Data

Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren

Comments in Journal of Machine Learning Research (JMLR), 2016

Journal ref Journal of Machine Learning Research 17 (2016) 1-54

Characteristic Kernels and Infinitely Divisible Distributions

Yu Nishiyama, Kenji Fukumizu

Journal ref Journal of Machine Learning Research 17(180):1-28, 2016

Learning Theory for Distribution Regression

Zoltan Szabo, Bharath Sriperumbudur, Barnabas Poczos, Arthur Gretton

Comments Final version appeared at JMLR, with supplement. Code: https://bitbucket.org/szzoli/ite/. arXiv admin note: text overlap with arXiv:1402.1754

Journal ref Journal of Machine Learning Research, 17(152):1-40, 2016

Feature-Level Domain Adaptation

Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten

Comments 32 pages, 13 figures, 9 tables

Journal ref JMLR 17:171 (2016) 1-32

On the Influence of Momentum Acceleration on Online Learning

Kun Yuan, Bicheng Ying, Ali H. Sayed

Comments 66 pages, 9 figures, to appear in Journal of Machine Learning Research, 2016

Gamma Belief Networks

Mingyuan Zhou, Yulai Cong, Bo Chen

Comments 44 pages, 24 figures

Journal ref Journal of Machine Learning Research, 17(163):1-44, September 2016

Minimum Density Hyperplanes

Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis

Journal ref Journal of Machine Learning Research, 17(156): 1-33, 2016

Hawkes Processes with Stochastic Excitations

Young Lee, Kar Wai Lim, Cheng Soon Ong

Comments Copy of ICML paper

Journal ref Proceedings of The 33rd International Conference on Machine Learning (ICML), pp. 79-88. JMLR. 2016

Bibliographic Analysis with the Citation Network Topic Model

Kar Wai Lim, Wray Buntine

Comments A copy of ACML paper. arXiv admin note: substantial text overlap with arXiv:1609.06532

Journal ref Proceedings of the Sixth Asian Conference on Machine Learning (ACML), pp. 142-158. JMLR. 2014

Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models

Bernardo Ávila Pires, Csaba Szepesvári

Comments 30 pages. Corrected typos. Appears in JMLR Workshop and Conference Proceedings 49: Proceedings of the 29th Annual Conference on Learning Theory (COLT 2016)

Journal ref JMLR W&CP 49: COLT 2016 Proceedings (2016) 1-31

Refined Error Bounds for Several Learning Algorithms

Steve Hanneke

Journal ref Journal of Machine Learning Research, Vol. 17 (2016), No. 135, pp. 1-55

The Optimal Sample Complexity of PAC Learning

Steve Hanneke

Journal ref Journal of Machine Learning Research, Vol. 17 (2016), No. 38, pp. 1-15

True Online Temporal-Difference Learning

Harm van Seijen, A. Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton

Comments This is the published JMLR version. It is a much improved version. The main changes are: 1) re-structuring of the article; 2) additional analysis on the forward view; 3) empirical comparison of traditional and new forward view; 4) added discussion of other true online papers; 5) updated discussion for non-linear function approximation

Journal ref Journal of Machine Learning Research (JMLR), 17(145):1-40, 2016

Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits

Gergely Neu, Gábor Bartók

Comments To appear in JMLR

Online Learning to Rank with Top-k Feedback

Sougata Chaudhuri, Ambuj Tewari

Comments Under review in JMLR

Generalization error bounds for learning to rank: Does the length of document lists matter?

Ambuj Tewari, Sougata Chaudhuri

Comments Appeared in ICML 2015. arXiv admin note: substantial text overlap with arXiv:1405.0586

Journal ref ICML 2015, volume 37 of JMLR Workshop and Conference Proceedings, pg.- 315-323, 2015

Online Learning to Rank with Feedback at the Top

Sougata Chaudhuri, Ambuj Tewari

Comments Appearing in AISTATS 2016

Journal ref AISTATS 16, volume 51 of JMLR Workshop and Conference Proceedings, pg.-277-285, 2016

Online Ranking with Top-1 Feedback

Sougata Chaudhuri, Ambuj Tewari

Comments Previous version being replaced by conference version. Appeared in AISTATS 2015

Journal ref AISTATS 15, volume 38 of JMLR Workshop and Conference Proceedings, pg.- 129-137, 2015

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

Ha Quang Minh, Loris Bazzani, Vittorio Murino

Comments 72 pages

Journal ref Journal of Machine Learning Research 17 (2016) 1-72

String and Membrane Gaussian Processes

Yves-Laurent Kom Samo, Stephen Roberts

Comments To appear in the Journal of Machine Learning Research (JMLR), Volume 17

Causal Discovery with Continuous Additive Noise Models

Jonas Peters, Joris Mooij, Dominik Janzing, Bernhard Schölkopf

Journal ref Journal of Machine Learning Research 15:2009-2053, 2014

Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models

Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther

Journal ref Journal of Machine Learning Research, 17(103):1-38, 2016

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning

Richard S. Sutton, A. Rupam Mahmood, Martha White

Comments 29 pages This is a significant revision based on the first set of reviews. The most important change was to signal early that the main result is about stability, not convergence

Journal ref Journal of Machine Learning Research 17(73): 1-29, 2016

Bootstrap-Based Regularization for Low-Rank Matrix Estimation

Julie Josse, Stefan Wager

Comments To appear in the Journal of Machine Learning Research

Model-Free Imitation Learning with Policy Optimization

Jonathan Ho, Jayesh K. Gupta, Stefano Ermon

Comments In Proceedings of the 33rd International Conference on Machine Learning, 2016

Journal ref JMLR W&CP 48 (2016) 2760-2769

Auxiliary Deep Generative Models

Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther

Comments Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016, JMLR: Workshop and Conference Proceedings volume 48, Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016

Trend Filtering on Graphs

Yu-Xiang Wang, James Sharpnack, Alex Smola, Ryan J. Tibshirani

Comments A short version appeared in AISTATS'2015

Journal ref Journal of Machine Learning Research Volume (2016) Volume 17 Article 15-147

Primal-Dual Rates and Certificates

Celestine Dünner, Simone Forte, Martin Takáč, Martin Jaggi

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

Neural Variational Inference and Learning in Belief Networks

Andriy Mnih, Karol Gregor

Journal ref Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32, 2014 pgs 1791-1799