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：昂贵黑箱函数的优化问题在众多科学领域普遍存在。贝叶斯优化作为一种自动、通用且样本高效的方法，能在对底层函数动力学知之甚少的情况下解决此类问题。然而，现有贝叶斯优化方法在融入关于目标函数的先验知识或信念以加速优化进程方面能力有限，这降低了其在预算紧张且经验丰富的实践者中的吸引力。为使领域专家能够定制优化流程，我们提出ColaBO——首个在贝叶斯原理下突破传统核结构局限、允许融入先验信念（如优化器可能位置或最优值）的框架。ColaBO的通用性使其适用于不同蒙特卡洛采集函数及多种用户信念类型。实验证明，当先验信息准确时，ColaBO能显著加速优化过程；即便先验信息存在误导，其性能仍可维持与默认方法相当的水平。