Sharp finite-sample concentration of independent variables
Machine Learning
2021-10-12 v5 Information Theory
math.IT
Probability
Machine Learning
Abstract
We show an extension of Sanov's theorem on large deviations, controlling the tail probabilities of i.i.d. random variables with matching concentration and anti-concentration bounds. This result has a general scope, applies to samples of any size, and has a short information-theoretic proof using elementary techniques.
Keywords
Cite
@article{arxiv.2008.13293,
title = {Sharp finite-sample concentration of independent variables},
author = {Akshay Balsubramani},
journal= {arXiv preprint arXiv:2008.13293},
year = {2021}
}