Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are computed from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI) function. The need to ensure analytical tractability of the predictive often poses limitations that can hinder the efficiency and applicability of BO. In this paper, we cast the computation of EI as a binary classification problem, building on the link between class-probability estimation and density-ratio estimation, and the lesser-known link between density-ratios and EI. By circumventing the tractability constraints, this reformulation provides numerous advantages, not least in terms of expressiveness, versatility, and scalability.

翻译：贝叶斯优化(BO)是最有效和广泛使用的黑匣子优化方法之一。BO根据一种在获取功能中编码的探索-开发性权衡标准提出解决方案,其中许多是根据概率替代模型的后期预测计算的。其中的先导功能是预期的改进功能(EI),需要确保预测的可分析性,这往往带来限制,可能妨碍BO的效率和适用性。在本文中,我们将计算EI作为一个二元分类问题,以分类概率估计和密度-比率估计之间的联系以及密度-比率与EI之间的较不为人所知的联系为基础。通过绕过可移动性限制,这种重新拟订提供了许多好处,特别是表达性、多功能性和可伸缩性。