Uniform Generalization, Concentration, and Adaptive Learning

TL;DR

This paper explores the concept of uniform generalization in learning algorithms, establishing its connection to concentration inequalities and providing theoretical bounds and tightness results.

Contribution

It proves that uniform generalization in expectation implies concentration, introduces a chain rule for uniform generalization risk, and derives a tight large deviation bound.

Findings

Uniform generalization in expectation implies concentration.

A chain rule for the uniform generalization risk is established.

A tight large deviation bound is derived.

Abstract

One fundamental goal in any learning algorithm is to mitigate its risk for overfitting. Mathematically, this requires that the learning algorithm enjoys a small generalization risk, which is defined either in expectation or in probability. Both types of generalization are commonly used in the literature. For instance, generalization in expectation has been used to analyze algorithms, such as ridge regression and SGD, whereas generalization in probability is used in the VC theory, among others. Recently, a third notion of generalization has been studied, called uniform generalization, which requires that the generalization risk vanishes uniformly in expectation across all bounded parametric losses. It has been shown that uniform generalization is, in fact, equivalent to an information-theoretic stability constraint, and that it recovers classical results in learning theory. It is achievable under various settings, such as sample compression schemes, finite hypothesis spaces, finite domains, and differential privacy. However, the relationship between uniform generalization and concentration remained unknown. In this paper, we answer this question by proving that, while a generalization in expectation does not imply a generalization in probability, a uniform generalization in expectation does imply concentration. We establish a chain rule for the uniform generalization risk of the composition of hypotheses and use it to derive a large deviation bound. Finally, we prove that the bound is tight.