The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient method to solve these problems with minimal knowledge of the underlying function dynamics. However, the ability of Bayesian optimization to incorporate prior knowledge or beliefs about the function at hand in order to accelerate the optimization is limited, which reduces its appeal for knowledgeable practitioners with tight budgets. To allow domain experts to customize the optimization routine, we propose ColaBO, the first Bayesian-principled framework for incorporating prior beliefs beyond the typical kernel structure, such as the likely location of the optimizer or the optimal value. The generality of ColaBO makes it applicable across different Monte Carlo acquisition functions and types of user beliefs. We empirically demonstrate ColaBO's ability to substantially accelerate optimization when the prior information is accurate, and to retain approximately default performance when it is misleading.

翻译：对昂贵黑箱函数的优化在诸多科学领域中普遍存在。贝叶斯优化是一种自动化、通用且样本高效的方法，能在对底层函数动态信息知之甚少的情况下解决此类问题。然而，贝叶斯优化在结合关于待优化函数的先验知识或信念以加速优化方面的能力有限，这降低了其对预算紧张且经验丰富的实践者的吸引力。为使领域专家能够自定义优化过程，我们提出ColaBO——首个基于贝叶斯原理的框架，用于整合超越典型核结构的先验信念，例如优化器可能的位置或最优值。ColaBO的通用性使其可适用于不同的蒙特卡洛采集函数类型及用户信念形式。我们通过实验证明，当先验信息准确时，ColaBO能够显著加速优化过程，而在先验信息具有误导性时，其性能仍能大致保持默认水平。