A General Framework for User-Guided Bayesian Optimization

TL;DR

This paper introduces ColaBO, a flexible Bayesian optimization framework that allows users to incorporate prior knowledge, improving efficiency when the prior is accurate and maintaining robustness when it is not.

Contribution

ColaBO is the first Bayesian framework enabling incorporation of diverse prior beliefs beyond kernel structures, applicable across various acquisition functions.

Findings

ColaBO accelerates optimization with accurate priors.

It maintains default performance with misleading priors.

The framework is broadly applicable across different beliefs and acquisition functions.

Abstract

The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient method to solve these problems with minimal knowledge of the underlying function dynamics. However, the ability of Bayesian optimization to incorporate prior knowledge or beliefs about the function at hand in order to accelerate the optimization is limited, which reduces its appeal for knowledgeable practitioners with tight budgets. To allow domain experts to customize the optimization routine, we propose ColaBO, the first Bayesian-principled framework for incorporating prior beliefs beyond the typical kernel structure, such as the likely location of the optimizer or the optimal value. The generality of ColaBO makes it applicable across different Monte Carlo acquisition functions and types of user beliefs. We empirically demonstrate ColaBO's ability to substantially accelerate optimization when the prior information is accurate, and to retain approximately default performance when it is misleading.