NeurIPS 2026Poster
On Concentration Inequalities for Sampling without Replacement
作者: Shaojie Li, Bowei Zhu, Yong Liu
摘要
This paper studies concentration inequalities for sampling without replacement. We introduce a general inequality that provides simple guidelines for proving concentration inequalities, offers a degree of unification of existing results, and is strong enough to yield sharper bounds. As applications, we revisit several standard statistics and either recover classical inequalities through streamlined proofs or strengthen them to tighter bounds, with rates optimal up to constants.