Optimal Approximations Made Easy
Machine Learning
2022-09-02 v3 Computational Geometry
Combinatorics
Machine Learning
Abstract
The fundamental result of Li, Long, and Srinivasan on approximations of set systems has become a key tool across several communities such as learning theory, algorithms, computational geometry, combinatorics and data analysis. The goal of this paper is to give a modular, self-contained, intuitive proof of this result for finite set systems. The only ingredient we assume is the standard Chernoff's concentration bound. This makes the proof accessible to a wider audience, readers not familiar with techniques from statistical learning theory, and makes it possible to be covered in a single self-contained lecture in a geometry, algorithms or combinatorics course.
Cite
@article{arxiv.2008.08970,
title = {Optimal Approximations Made Easy},
author = {Mónika Csikós and Nabil H. Mustafa},
journal= {arXiv preprint arXiv:2008.08970},
year = {2022}
}