Bayesian optimization is a powerful global optimization technique for
expensive black-box functions. One of its shortcomings is that it requires
auxiliary optimization of an acquisition function at each iteration. This
auxiliary optimization can be costly and very hard to carry out in practice.
Moreover, it creates serious theoretical concerns, as most of the convergence
results assume that the exact optimum of the acquisition function can be found.
In this paper, we introduce a new technique for efficient global optimization
that combines Gaussian process confidence bounds and treed simultaneous
optimistic optimization to eliminate the need for auxiliary optimization of
acquisition functions. The experiments with global optimization benchmarks and
a novel application to automatic information extraction demonstrate that the
resulting technique is more efficient than the two approaches from which it
draws inspiration. Unlike most theoretical analyses of Bayesian optimization
with Gaussian processes, our finite-time convergence rate proofs do not require
exact optimization of an acquisition function. That is, our approach eliminates
the unsatisfactory assumption that a difficult, potentially NP-hard, problem
has to be solved in order to obtain vanishing regret rates.

本文介绍了一种将高斯过程置信度边界和树形同时乐观的优化相结合的技术，以消除对收购功能辅助优化的需求，并且相比于其他方法更为有效。