Web: http://arxiv.org/abs/2110.15073

June 23, 2022, 1:11 a.m. | Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton

cs.LG updates on arXiv.org arxiv.org

We propose a novel nonparametric two-sample test based on the Maximum Mean
Discrepancy (MMD), which is constructed by aggregating tests with different
kernel bandwidths. This aggregation procedure, called MMDAgg, ensures that test
power is maximised over the collection of kernels used, without requiring
held-out data for kernel selection (which results in a loss of test power), or
arbitrary kernel choices such as the median heuristic. We work in the
non-asymptotic framework, and prove that our aggregated test is minimax
adaptive …

arxiv ml test

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