Batched Large-scale Bayesian Optimization in High-dimensional Spaces

Understand

Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries.

• However, many cases, such as the ones with high-dimensional inputs, may require a much larger number of observations for optimization.
• Despite an abundance of observations thanks to parallel experiments, current BO techniques have been limited to merely a few thousand observations.
• In this paper, we propose ensemble Bayesian optimization (EBO) to address three current challenges in BO simultaneously: (1) large-scale observations; (2) high dimensional input spaces; and (3) selections of batch queries that balance quality and diversity.