Web: http://arxiv.org/abs/2206.07892

June 17, 2022, 1:10 a.m. | Margalit Glasgow, Colin Wei, Mary Wootters, Tengyu Ma

cs.LG updates on arXiv.org arxiv.org

A major challenge in modern machine learning is theoretically understanding
the generalization properties of overparameterized models. Many existing tools
rely on \em uniform convergence \em (UC), a property that, when it holds,
guarantees that the test loss will be close to the training loss, uniformly
over a class of candidate models. Nagarajan and Kolter (2019) show that in
certain simple linear and neural-network settings, any uniform convergence
bound will be vacuous, leaving open the question of how to prove generalization …

arxiv convergence lg uniform

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY