Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables

TL;DR

This paper introduces a new split-kl concentration inequality tailored for ternary random variables, outperforming existing inequalities in certain regimes and addressing open questions in the field.

Contribution

It presents the split-kl inequality for ternary variables, a PAC-Bayes extension, and compares its performance with existing inequalities in various learning scenarios.

Findings

Split-kl inequality is competitive with kl and Bernstein inequalities.

Outperforms existing inequalities in specific regimes for ternary variables.

Provides the first direct comparison of Empirical Bernstein and Unexpected Bernstein inequalities.

Abstract

We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality. The inequality is particularly well-suited for ternary random variables, which naturally show up in a variety of problems, including analysis of excess losses in classification, analysis of weighted majority votes, and learning with abstention. We demonstrate that for ternary random variables the inequality is simultaneously competitive with the kl inequality, the Empirical Bernstein inequality, and the Unexpected Bernstein inequality, and in certain regimes outperforms all of them. It resolves an open question by Tolstikhin and Seldin [2013] and Mhammedi et al. [2019] on how to match simultaneously the combinatorial power of the kl inequality when the distribution happens to be close to binary and the power of Bernstein inequalities to exploit low variance when the probability mass is concentrated on the middle value. We also derive a PAC-Bayes-split-kl inequality and compare it with the PAC-Bayes-kl, PAC-Bayes-Empirical-Bennett, and PAC-Bayes-Unexpected-Bernstein inequalities in an analysis of excess losses and in an analysis of a weighted majority vote for several UCI datasets. Last but not least, our study provides the first direct comparison of the Empirical Bernstein and Unexpected Bernstein inequalities and their PAC-Bayes extensions.