Bayesian Optimization (BO) is typically used to optimize an unknown function
$f$ that is noisy and costly to evaluate, by exploiting an acquisition function
that must be maximized at each optimization step. Although provably
asymptotically optimal BO algorithms are efficient at optimizing
low-dimensional functions, scaling them to high-dimensional spaces remains an
open research problem, often tackled by assuming an additive structure for $f$.
However, such algorithms introduce additional restrictive assumptions on the
additive structure that reduce their applicability domain. In this paper, we
relax the restrictive assumptions on the additive structure of $f$, at the
expense of weakening the maximization guarantees of the acquisition function,
and we address the over-exploration problem for decentralized BO algorithms. To
these ends, we propose DuMBO, an asymptotically optimal decentralized BO
algorithm that achieves very competitive performance against state-of-the-art
BO algorithms, especially when the additive structure of $f$ does not exist or
comprises high-dimensional factors.

本文提出了一种松弛针对 $f$ 的加性结构限制的去中心化贝叶斯优化算法 DuMBO，并针对过度探索问题进行调节，是一种在高维环境下实现成本高昂的优化的良好选择。