BORE: Bayesian Optimization by Density-Ratio Estimation
This addresses efficiency and applicability issues in blackbox optimization for researchers and practitioners, though it appears incremental as it builds on existing BO methods.
The paper tackles the limitations of Bayesian optimization (BO) due to tractability constraints in acquisition functions like expected improvement (EI) by reformulating EI computation as a binary classification problem using density-ratio estimation, resulting in improved expressiveness, versatility, and scalability.
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.