JMLR - arXivDaily 期刊&会议

2003.09198 2021-10-12 stat.ML cs.LG

A unified framework for spectral clustering in sparse graphs

Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay

Journal ref Journal of Machine Learning Research 22(217) (2021) 1-56

1811.03179 2021-10-12 math.ST cs.LG stat.ML stat.TH

How Well Generative Adversarial Networks Learn Distributions

Tengyuan Liang

Comments Journal of Machine Learning Research, to appear

Journal ref Journal of Machine Learning Research 22 (2021) 1-41

2007.12911 2021-09-23 cs.LG cs.CV stat.ML

Tighter risk certificates for neural networks

María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári

Comments New version includes: i) experiment showing the potential of the risk certificate for neural architecture search (Fig. 2); ii) experiments spanning uncertainty quantification and analysis of prior/posterior (Section 7.8); iii) an outline of the strengths of probabilistic neural networks trained by PBB (Section 7.9) and iv) a strengthened discussion on the connection to Bayesian learning

Journal ref Journal of Machine Learning Research, 2021

2007.03253 2021-09-21 stat.ML cs.LG math.ST stat.TH

Doubly infinite residual neural networks: a diffusion process approach

Stefano Peluchetti, Stefano Favaro

Journal ref Journal of Machine Learning Research 22; (175):1-48, 2021

2002.08246 2021-09-21 math.OC cs.LG stat.ML

A Unified Convergence Analysis for Shuffling-Type Gradient Methods

Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk

Comments Journal of Machine Learning Research, 2021

1902.02060 2021-09-16 cs.LG math.OC stat.ML

On ADMM in Deep Learning: Convergence and Saturation-Avoidance

Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou

Comments This is a revised version of our previous one entitled "A Convergence Analysis of Nonlinearly Constrained ADMM in Deep Learning, arXiv:1902.02060" with some significantly changes

Journal ref Journal of Machine Learning Research 22 (2021) 1-67

2006.02585 2021-09-07 cs.LG math.OC stat.ML

Online mirror descent and dual averaging: keeping pace in the dynamic case

Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander

Comments 27 pages main text, 37 pages in total, 1 figure. Version 2: Revision for camera-ready version of ICML 2020, with a new abstract, new discussion and acknowledgements sections, and some other minor modifications. Version 3: Technical report version of JMLR submission, with minor revisions, full proofs, and more details on the setting with composite functions

2006.09268 2021-09-06 cs.LG math.PR math.ST stat.ML stat.TH

Metrizing Weak Convergence with Maximum Mean Discrepancies

Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey

Comments 14 pages. Corrects in particular Thm.12 of Simon-Gabriel and Schölkopf, JMLR, 19(44):1-29, 2018. See http://jmlr.org/papers/v19/16-291.html

2102.13176 2021-09-03 cs.LG

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Journal ref Journal of Machine Learning Research 22 (182), Pages 1-52, 2021

2002.11219 2021-09-01 cs.LG stat.ML

Convex Geometry and Duality of Over-parameterized Neural Networks

Tolga Ergen, Mert Pilanci

Comments Accepted to the Journal of Machine Learning Research (JMLR)

1905.05976 2021-09-01 math.ST cs.LG stat.ML stat.TH

Information criteria for non-normalized models

Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen

Journal ref Journal of Machine Learning Research, 22(158):1--33, 2021

2102.06622 2021-08-31 cs.LG stat.ML

MetaGrad: Adaptation using Multiple Learning Rates in Online Learning

Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven

Journal ref Journal of Machine Learning Research 22(161):1-61, 2021

1507.02592 2021-08-31 cs.LG stat.ML

Fast rates in statistical and online learning

Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson

Comments 69 pages, 3 figures

Journal ref Journal of Machine Learning Research 6(54):1793-1861, 2015

1301.0534 2021-08-31 cs.LG stat.ML

Follow the Leader If You Can, Hedge If You Must

Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen

Comments under submission

Journal ref Journal of Machine Learning Research 15(37):1281-1316, 2014

2012.04456 2021-08-27 cs.LG stat.ML

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik

Journal ref Journal of Machine Learning Research 22(2021) 1-73

2012.01758 2021-08-18 stat.ME math.ST stat.TH

Non-parametric Quantile Regression via the K-NN Fused Lasso

Steven Siwei Ye, Oscar Hernan Madrid Padilla

Journal ref Journal of Machine Learning Research, Vol. 22, No. 111, 1-38, 2021

2006.07034 2021-08-18 cs.CV

Benchmarking Unsupervised Object Representations for Video Sequences

Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker

Journal ref Journal of Machine Learning Research 22 (183): 1-61, 2021

1905.12278 2021-08-17 cs.LG math.OC stat.ML

An Inertial Newton Algorithm for Deep Learning

Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels

Comments To appear in Journal of Machine Learning Research (JMLR), Volume 22, acceptance date: 5/21

Journal ref Journal of Machine Learning Research (JMLR), v22(134):1-31, 2021

2011.04026 2021-08-03 stat.ML cs.LG math.ST stat.TH

Pathwise Conditioning of Gaussian Processes

James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth

Journal ref Journal of Machine Learning Research, 22(105):1-47, 2021

1811.11891 2021-07-30 stat.ML cs.LG

Manifold Coordinates with Physical Meaning

Samson Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen

Comments Submitted to JMLR. Improved over v2 (added appendix). Improved over v1 (revisions)

1908.01089 2021-07-20 cs.LG cs.AI math.OC stat.ML

Path Length Bounds for Gradient Descent and Flow

Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas

Comments 55 pages. Accepted for publication at the Journal of Machine Learning Research (JMLR, 2021)

1807.11398 2021-07-13 cs.LG stat.ML

Preference-based Online Learning with Dueling Bandits: A Survey

Viktor Bengs, Robert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier

Comments 108 pages

Journal ref Journal of Machine Learning Research, 22(7):1-108, 2021

1810.10207 2021-07-08 math.OC stat.ML

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems

Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang

Comments Accepted by Journal of Machine Learning Research (JMLR)

2006.06130 2021-07-05 cs.LG stat.ML

ROOTS: Object-Centric Representation and Rendering of 3D Scenes

Chang Chen, Fei Deng, Sungjin Ahn

Comments First two authors contributed equally. Accepted in JMLR

1905.02515 2021-07-02 stat.ML cs.LG

Guided Visual Exploration of Relations in Data Sets

Kai Puolamäki, Emilia Oikarinen, Andreas Henelius

Comments 32 pages, 13 figures. This article extends arXiv:1804.03194 and arXiv:1805.07725

Journal ref Journal of Machine Learning Research 22(96):1-32, 2021

1803.06518 2021-06-29 stat.ME stat.AP stat.CO stat.ML

Provable Convex Co-clustering of Tensors

Eric C. Chi, Brian R. Gaines, Will Wei Sun, Hua Zhou, Jian Yang

Comments to appear in Journal of Machine Learning Research

Journal ref Journal of Machine Learning Research, 21(214):1-58, 2020

2011.06811 2021-06-24 cs.NE

Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning

Rasmus Berg Palm, Elias Najarro, Sebastian Risi

Comments JMLR 148, NeurIPS pre-registration workshop 2020

1911.02490 2021-06-24 cs.LG stat.ML

OpenML-Python: an extensible Python API for OpenML

Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter

Journal ref Journal of Machine Learning Research 22(100), 2021

1802.02698 2021-06-24 stat.ME

More Efficient Estimation for Logistic Regression with Optimal Subsample

HaiYing Wang

Journal ref Journal of Machine Learning Research, 2019

1210.6293 2021-06-24 cs.MS cs.CV cs.LG

MLPACK: A Scalable C++ Machine Learning Library

Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray

Comments Submitted to JMLR MLOSS (http://jmlr.csail.mit.edu/mloss/)

Journal ref Journal of Machine Learning Research 14 (2013) 801-805