AISTATS 2021poster10 citations
Robust hypothesis testing and distribution estimation in Hellinger distance
Abstract
We propose a simple robust hypothesis test that has the same sample complexity as that of the optimal Neyman-Pearson test up to constants, but robust to distribution perturbations under Hellinger distance. We discuss the applicability of such a robust test for estimating distributions in Hellinger distance. We empirically demonstrate the power of the test on canonical distributions.
BibTeX
@InProceedings{pmlr-v130-theertha-suresh21a,
title = { Robust hypothesis testing and distribution estimation in Hellinger distance },
author = {Theertha Suresh, Ananda},
booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
pages = {2962--2970},
year = {2021},
editor = {Banerjee, Arindam and Fukumizu, Kenji},
volume = {130},
series = {Proceedings of Machine Learning Research},
month = {13--15 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v130/theertha-suresh21a/theertha-suresh21a.pdf},
url = {https://proceedings.mlr.press/v130/theertha-suresh21a.html},
abstract = { We propose a simple robust hypothesis test that has the same sample complexity as that of the optimal Neyman-Pearson test up to constants, but robust to distribution perturbations under Hellinger distance. We discuss the applicability of such a robust test for estimating distributions in Hellinger distance. We empirically demonstrate the power of the test on canonical distributions. }
}