Concentration Bounds for Discrete Distribution Estimation in KL Divergence
Machine Learning
2023-06-14 v2 Discrete Mathematics
Information Theory
Machine Learning
math.IT
Probability
Abstract
We study the problem of discrete distribution estimation in KL divergence and provide concentration bounds for the Laplace estimator. We show that the deviation from mean scales as when , improving upon the best prior result of . We also establish a matching lower bound that shows that our bounds are tight up to polylogarithmic factors.
Keywords
Cite
@article{arxiv.2302.06869,
title = {Concentration Bounds for Discrete Distribution Estimation in KL Divergence},
author = {Clément L. Canonne and Ziteng Sun and Ananda Theertha Suresh},
journal= {arXiv preprint arXiv:2302.06869},
year = {2023}
}
Comments
Updated discussion of previous work