  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.
The key idea of EBO is to operate on an ensemble of additive Gaussian process
models, each of which possesses a randomized strategy to divide and conquer. We
show unprecedented, previously impossible results of scaling up BO to tens of
thousands of observations within minutes of computation.
