Batched High-dimensional Bayesian Optimization via Structural Kernel Learning

TL;DR

This paper introduces a novel high-dimensional Bayesian optimization method that leverages latent additive structures and parallel evaluations to improve efficiency and effectiveness, demonstrated through synthetic and real-world experiments.

Contribution

It proposes a new approach that learns latent additive structures with Gibbs sampling and constructs batched queries using determinantal point processes for high-dimensional BO.

Findings

Outperforms existing state-of-the-art methods on synthetic functions.

Effectively handles high-dimensional black-box optimization.

Reduces number of iterations through parallel evaluations.

Abstract

Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of iterations required by the method. Our novel approach learns the latent structure with Gibbs sampling and constructs batched queries using determinantal point processes. Experimental validations on both synthetic and real-world functions demonstrate that the proposed method outperforms the existing state-of-the-art approaches.