Bayesian Optimization of Composite Functions

TL;DR

This paper introduces a novel Bayesian optimization method tailored for composite functions, leveraging their structure with multi-output Gaussian processes and a new expected improvement criterion, leading to significant efficiency gains.

Contribution

It proposes a new Bayesian optimization approach that exploits the composite structure of functions, including a stochastic gradient estimator for the expected improvement, and proves its asymptotic optimality.

Findings

Dramatic reduction in simple regret compared to benchmarks

Efficient sampling through a novel stochastic gradient estimator

Asymptotic consistency of the proposed method

Abstract

We consider optimization of composite objective functions, i.e., of the form $f(x)=g(h(x))$, where $h$ is a black-box derivative-free expensive-to-evaluate function with vector-valued outputs, and $g$ is a cheap-to-evaluate real-valued function. While these problems can be solved with standard Bayesian optimization, we propose a novel approach that exploits the composite structure of the objective function to substantially improve sampling efficiency. Our approach models $h$ using a multi-output Gaussian process and chooses where to sample using the expected improvement evaluated on the implied non-Gaussian posterior on $f$, which we call expected improvement for composite functions (\ei). Although \ei\ cannot be computed in closed form, we provide a novel stochastic gradient estimator that allows its efficient maximization. We also show that our approach is asymptotically consistent, i.e., that it recovers a globally optimal solution as sampling effort grows to infinity, generalizing previous convergence results for classical expected improvement. Numerical experiments show that our approach dramatically outperforms standard Bayesian optimization benchmarks, reducing simple regret by several orders of magnitude.