Concentration Inequalities for Random Sets
Abstract
In a large, possibly infinite population, each subject is colored red with probability , independently of the others. Then, a finite sub-population is selected, possibly as a function of the coloring. The imbalance in the sub-population is defined as the difference between the number of reds in it and p times its size. This paper presents high-probability upper bounds (tail-bounds) on this imbalance. To present the upper bounds we define the *UI dimension* --- a new measure for the richness of a set-family. We present three simple rules for upper-bounding the UI dimension of a set-family. Our upper bounds on the imbalance in a sub-population depend only on the size of the sub-population and on the UI dimension of its support. We relate our results to known concepts from machine learning, particularly the VC dimension and Rademacher complexity.
Cite
@article{arxiv.1612.08580,
title = {Concentration Inequalities for Random Sets},
author = {Erel Segal-Halevi and Avinatan Hassidim},
journal= {arXiv preprint arXiv:1612.08580},
year = {2016}
}