Concentration-Bound Analysis for Probabilistic Programs and Probabilistic Recurrence Relations

TL;DR

This paper introduces a novel method using exponential supermartingales to derive tighter concentration bounds for probabilistic programs and recurrences, surpassing classical techniques like Azuma's inequality and Karp's method.

Contribution

The authors develop a new approach for concentration bounds via exponential supermartingales, with algorithms for synthesis and improved bounds over existing methods.

Findings

Derives tighter concentration bounds than Azuma's inequality.

Achieves better bounds than Karp's classical recurrence analysis.

Matches near-optimal bounds for quicksort.

Abstract

Analyzing probabilistic programs and randomized algorithms are classical problems in computer science. The first basic problem in the analysis of stochastic processes is to consider the expectation or mean, and another basic problem is to consider concentration bounds, i.e. showing that large deviations from the mean have small probability. Similarly, in the context of probabilistic programs and randomized algorithms, the analysis of expected termination time/running time and their concentration bounds are fundamental problems.In this work, we focus on concentration bounds for probabilistic programs and probabilistic recurrences of randomized algorithms. For probabilistic programs, the basic technique to achieve concentration bounds is to consider martingales and apply the classical Azuma's inequality. For probabilistic recurrences of randomized algorithms, Karp's classical "cookbook" method, which is similar to the master theorem for recurrences, is the standard approach to obtain concentration bounds. In this work, we propose a novel approach for deriving concentration bounds for probabilistic programs and probabilistic recurrence relations through the synthesis of exponential supermartingales. For probabilistic programs, we present algorithms for synthesis of such supermartingales in several cases. We also show that our approach can derive better concentration bounds than simply applying the classical Azuma's inequality over various probabilistic programs considered in the literature. For probabilistic recurrences, our approach can derive tighter bounds than the Karp's well-established methods on classical algorithms. Moreover, we show that our approach could derive bounds comparable to the optimal bound for quicksort, proposed by McDiarmid and Hayward. We also present a prototype implementation that can automatically infer these bounds