JMLR

Deep Exploration via Randomized Value Functions

Ian Osband, Benjamin Van Roy, Daniel Russo, Zheng Wen

Comments Accepted for publication in Journal of Machine Learning Research 2019

Differentially Private Regression and Classification with Sparse Gaussian Processes

Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence

Comments 26 pages, 6 figures. Submitted to JMLR 4th January, 2019 (in review)

Time-to-Event Prediction with Neural Networks and Cox Regression

Håvard Kvamme, Ørnulf Borgan, Ida Scheel

Journal ref Journal of Machine Learning Research, 20(129):1-30, 2019

Gaussian processes with linear operator inequality constraints

Christian Agrell

Comments Published in JMLR: http://jmlr.org/papers/volume20/19-065/19-065.pdf

Journal ref Journal of Machine Learning Research (JMLR), 20(135):1--36, 2019

Generalized Integrated Gradients: A practical method for explaining diverse ensembles

John Merrill, Geoff Ward, Sean Kamkar, Jay Budzik, Douglas Merrill

Comments 38 pages, submitted to JMLR 9/3/2019

Nonparametric Bayesian Aggregation for Massive Data

Zuofeng Shang, Botao Hao, Guang Cheng

Comments To appear in Journal of Machine Learning Research. arXiv admin note: text overlap with arXiv:1411.3686

Cells in Multidimensional Recurrent Neural Networks

G. Leifert, T. Strauß, T. Grüning, R. Labahn

Journal ref Journal of Machine Learning Research 17 (2016) 1-37

Long-term Forecasting using Higher Order Tensor RNNs

Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue

Comments 24 pages including appendix, updated JMLR version

Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling

Omer Weissbrod, Shachar Kaufman, David Golan, Saharon Rosset

Journal ref JMLR (108):1-30, 2019

Fisher consistency for prior probability shift

Dirk Tasche

Comments 28 pages, 2 figures, 8 tables, introduction extended

Journal ref Journal of Machine Learning Research 18, 3338-3369, 2017

Measuring the Effects of Data Parallelism on Neural Network Training

Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl

Journal ref Journal of Machine Learning Research 20 (2019) 1-49

Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction

Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang

Comments accepted by JMLR

On the optimality of the Hedge algorithm in the stochastic regime

Jaouad Mourtada, Stéphane Gaïffas

Journal ref Journal of Machine Learning Research, 20(83), 2019

Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces

Stephen Page, Steffen Grünewälder

Comments 49 pages, submitted to The Journal of Machine Learning Research

PyOD: A Python Toolbox for Scalable Outlier Detection

Yue Zhao, Zain Nasrullah, Zheng Li

Comments 7 pages, 1 figure, version 2 (published in JMLR Volume 20, MLOSS track)

Journal ref Journal of Machine Learning Research (JMLR), 20(96):1-7, 2019

Empirical Risk Minimization under Random Censorship: Theory and Practice

Guillaume Ausset, Stéphan Clémençon, François Portier

Comments Submitted to JMLR. 18 pages + Appendix

Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks

Peter L. Bartlett, Nick Harvey, Chris Liaw, Abbas Mehrabian

Comments Extended abstract appeared in COLT 2017; the upper bound was presented at the 2016 ACM Conference on Data Science. This version includes all the proofs and a refinement of the upper bound, Theorem 6. 16 pages, 2 figures

Journal ref Journal of Machine Learning Research 20 (2019) 1-17

A complete characterization of optimal dictionaries for least squares representation

Mohammed Rayyan Sheriff, Debasish Chatterjee

Comments 36 pages

Journal ref Journal of Machine Learning Research, Vol 18, Paper No. 107, 1--28, 2017

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

Benjamin Guedj, Bhargav Srinivasa Desikan

Journal ref Journal of Machine Learning Research (JMLR), 18(190):1--5, 2018

Generalized Score Matching for Non-Negative Data

Shiqing Yu, Mathias Drton, Ali Shojaie

Comments 70 pages, 76 figures

Journal ref Journal of Machine Learning Research, 20(76):1-70, 2019

Proximal Distance Algorithms: Theory and Examples

Kevin L. Keys, Hua Zhou, Kenneth Lange

Comments 23 pages, 2 figures, 7 tables

Journal ref Journal of Machine Learning Research 20:66, 1-38, 2009

Differentiable Game Mechanics

Alistair Letcher, David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

Comments JMLR 2019, journal version of arXiv:1802.05642

Journal ref Journal of Machine Learning Research (JMLR), v20 (84) 1-40, 2019

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

Christian Donner, Manfred Opper

Comments 34 pages; 6 figures

Journal ref Journal of Machine Learning Research, year 2018, volume 19,number 67, pages 1-34

Neural Architecture Search: A Survey

Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

Journal ref Journal of Machine Learning Research 20 (2019) 1-21

A tutorial on conformal prediction

Glenn Shafer, Vladimir Vovk

Comments 58 pages, 9 figures

Journal ref Journal of Machine Learning Research 9 (2008) 371-421. http://www.jmlr.org/papers/v9/shafer08a.html

Decontamination of Mutual Contamination Models

Julian Katz-Samuels, Gilles Blanchard, Clayton Scott

Comments Published in JMLR. Subsumes arXiv:1602.06235

Spectral Clustering Based on Local PCA

Ery Arias-Castro, Gilad Lerman, Teng Zhang

Journal ref Journal of Machine Learning Research, 18(9):1-57, 2017

Improved asynchronous parallel optimization analysis for stochastic incremental methods

Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

Comments 67 pages, published in JMLR, can be found online at http://jmlr.org/papers/v19/17-650.html. arXiv admin note: substantial text overlap with arXiv:1606.04809

Optimal Rates for Multi-pass Stochastic Gradient Methods

Junhong Lin, Lorenzo Rosasco

Comments Fixed a typo in Eq (66)

Journal ref Journal of Machine Learning Research, 18:1-47, 2017

To tune or not to tune the number of trees in random forest?

Philipp Probst, Anne-Laure Boulesteix

Comments 20 pages, 4 figures

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