Fully explicit large deviation inequalities for empirical processes with applications to information-based complexity

TL;DR

This paper derives explicit large deviation inequalities for empirical processes indexed by VC classes and demonstrates their significance in information-based complexity theory.

Contribution

It provides fully explicit bounds for empirical processes with VC class indexing, advancing the theoretical tools in statistical learning and complexity analysis.

Findings

Explicit large deviation inequalities for VC classes

Applications to information-based complexity theory

Enhanced understanding of empirical process behavior

Abstract

In the present paper we obtain fully explicit large deviation inequalities for empirical processes indexed by a Vapnik--Chervonenkis class of sets (or functions). Furthermore we illustrate the importance of such results for the theory of information-based complexity.