JMLR

Power of Knockoff: The Impact of Ranking Algorithm, Augmented Design, and Symmetric Statistic

Zheng Tracy Ke, Jun S. Liu, Yucong Ma

Comments 67 pages, 13 figures

Journal ref Journal of Machine Learning Research, 2024

The ensmallen library for flexible numerical optimization

Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson

Journal ref Journal of Machine Learning Research, Vol. 22, No. 166, 2021

A Unified Theory of Diversity in Ensemble Learning

Danny Wood, Tingting Mu, Andrew Webb, Henry Reeve, Mikel Luján, Gavin Brown

Journal ref Journal of Machine Learning Research, 24(359), 2023

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization

Ömer Deniz Akyildiz, Sotirios Sabanis

Comments Accepted to Journal of Machine Learning Research (JMLR), 2024, to appear

CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds

Muhammad Ahmed Chaudhry, Lyna Kim, Jeremy Irvin, Yuzu Ido, Sonia Chu, Jared Thomas Isobe, Andrew Y. Ng, Duncan Watson-Parris

Comments 11 pages, 5 figures, submitted to Journal of Machine Learning Research

Rates of convergence for density estimation with generative adversarial networks

Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov

Comments To appear in Journal of Machine Learning Research

Sample-efficient Adversarial Imitation Learning

Dahuin Jung, Hyungyu Lee, Sungroh Yoon

Comments Published at JMLR (Journal of Machine Learning Research), A preliminary version of this manuscript was presented at Deep RL Workshop, NeurIPS 2022

Probabilistic Truly Unordered Rule Sets

Lincen Yang, Matthijs van Leeuwen

Comments Submitted to JMLR

FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction

Alexander Telepov, Artem Tsypin, Kuzma Khrabrov, Sergey Yakukhnov, Pavel Strashnov, Petr Zhilyaev, Egor Rumiantsev, Daniel Ezhov, Manvel Avetisian, Olga Popova, Artur Kadurin

Comments 37 pages, 10 figures, to be published in TMLR journal (https://www.jmlr.org/tmlr/)

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

Journal ref Journal of Machine Learning Research, 2024

On Biased Compression for Distributed Learning

Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan

Comments 50 pages, 9 figures, 5 tables, 22 theorems and lemmas, 7 new compression operators, 1 algorithm

Journal ref Journal of Machine Learning Research 2023: https://www.jmlr.org/papers/v24/21-1548.html

On the Generalization of Stochastic Gradient Descent with Momentum

Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang

Comments 56 pages, 14 figures. To appear in the Journal of Machine Learning Research (JMLR)

Compression, Generalization and Learning

Marco C. Campi, Simone Garatti

Comments https://www.jmlr.org/papers/v24/22-0605.html

Journal ref Journal of Machine Learning Research, 24(339):1-74, 2023

GANs as Gradient Flows that Converge

Yu-Jui Huang, Yuchong Zhang

Journal ref Journal of Machine Learning Research, Vol. 24 (2023), No. 217, pp. 1-40

A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines

Marco C. Campi, Simone Garatti

Comments https://www.jmlr.org/papers/v22/21-0641.html

Journal ref Journal of Machine Learning Research 22(288):1-38, 2021

A unified recipe for deriving (time-uniform) PAC-Bayes bounds

Ben Chugg, Hongjian Wang, Aaditya Ramdas

Comments 56 pages. Published in the Journal of Machine Learning Research, Volume 24 Issue 372

Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand

Journal ref Journal of Machine Learning Research, 2023, vol. 24, no 364, p. 1-50

Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks

Guy Hacohen, Daphna Weinshall

Comments JMLR 2022

Journal ref Journal of Machine Learning Research, 23(155):1-46, 2022

Theory of Curriculum Learning, with Convex Loss Functions

Daphna Weinshall, Dan Amir

Journal ref Journal of Machine Learning Research (JMLR), 21:1-19, Nov 2020

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause

Comments JMLR, 62 pages, text overlap with arXiv:2002.05551

Journal ref Journal of Machine Learning Research (24), 2023, 1-62

Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders

Lisa Bonheme, Marek Grzes

Comments the main paper of 20 pages plus an appendix; 29 pages in total. Published as a JMLR article. The final version is available at https://jmlr.org/papers/v24/21-1145.html

Journal ref Journal of Machine Learning Research 24 (2023): 1-30

Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling

Stefano Peluchetti

Journal ref Journal of Machine Learning Research 24(374):1-51, 2023

The Power of Contrast for Feature Learning: A Theoretical Analysis

Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang

Comments 78 pages, accepted by JMLR

Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables

Hamid Mousavi, Jakob Drefs, Florian Hirschberger, Jörg Lücke

Journal ref Journal of Machine Learning Research, 24(285), 1-59 (2023)

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash

Comments 31 pages, 6 figures, 1 table, accepted in JMLR

MAUVE Scores for Generative Models: Theory and Practice

Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui

Comments Published in Journal of Machine Learning Research

Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation

Guillaume Sagnol, Luc Pronzato

Journal ref Journal of Machine Learning Research, 24(307), pp.1--32, 2023

Diffeomorphic Learning

Laurent Younes

Journal ref Journal of Machine Learning Research: 21(220):1-28, 2020

Dimensionality Reduction and Wasserstein Stability for Kernel Regression

Stephan Eckstein, Armin Iske, Mathias Trabs

Comments Forthcoming in JMLR

Stability and L2-penalty in Model Averaging

Hengkun Zhu, Guohua Zou

Comments 47 pages, 14 figures, 1 table. This article was submitted to Journal of Machine Learning Research in July 2023