Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in isolation, there has been recent interest in speeding up BO by transferring knowledge across multiple related black-box functions. In this work, we introduce a method to automatically design the BO search space by relying on evaluations of previous black-box functions. We depart from the common practice of defining a set of arbitrary search ranges a priori by considering search space geometries that are learned from historical data. This simple, yet effective strategy can be used to endow many existing BO methods with transfer learning properties. Despite its simplicity, we show that our approach considerably boosts BO by reducing the size of the search space, thus accelerating the optimization of a variety of black-box optimization problems. In particular, the proposed approach combined with random search results in a parameter-free, easy-to-implement, robust hyperparameter optimization strategy. We hope it will constitute a natural baseline for further research attempting to warm-start BO.

本文提出一种自动设计BO搜索空间的方法，通过以前黑盒函数的评估结果，学习搜索空间几何形态，使传统的BO方法具有迁移学习属性，加速了各种黑盒优化问题的优化过程，结合随机搜索实现了无参数、易于实现、鲁棒的超参数优化策略。

学习搜索空间以进行贝叶斯优化：超参数迁移学习的另一种视角