Fast, Precise Thompson Sampling for Bayesian Optimization

TL;DR

This paper introduces Stagger Thompson Sampler (STS), an improved method for Bayesian optimization that outperforms existing Thompson sampling variants and other acquisition functions in efficiency and accuracy across various test functions.

Contribution

The paper presents STS, a novel, more precise Thompson sampling algorithm that reduces computation time and improves optimization performance in Bayesian settings.

Findings

STS outperforms TS, PSS, and other methods in numerical experiments.

STS matches PSS performance when used with MTV batching.

STS locates the maximizer more precisely with less computation.

Abstract

Thompson sampling (TS) has optimal regret and excellent empirical performance in multi-armed bandit problems. Yet, in Bayesian optimization, TS underperforms popular acquisition functions (e.g., EI, UCB). TS samples arms according to the probability that they are optimal. A recent algorithm, P-Star Sampler (PSS), performs such a sampling via Hit-and-Run. We present an improved version, Stagger Thompson Sampler (STS). STS more precisely locates the maximizer than does TS using less computation time. We demonstrate that STS outperforms TS, PSS, and other acquisition methods in numerical experiments of optimizations of several test functions across a broad range of dimension. Additionally, since PSS was originally presented not as a standalone acquisition method but as an input to a batching algorithm called Minimal Terminal Variance (MTV), we also demon-strate that STS matches PSS performance when used as the input to MTV.