A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization. Bayesian optimization (BO) is a powerful tool that models and optimizes such expensive "black-box" functions. However, at the beginning of optimization, vanilla Bayesian optimization methods often suffer from slow convergence issue due to inaccurate modeling based on few trials. To address this issue, researchers in the BO community propose to incorporate the spirit of transfer learning to accelerate optimization process, which could borrow strength from the past tasks (source tasks) to accelerate the current optimization problem (target task). This survey paper first summarizes transfer learning methods for Bayesian optimization from four perspectives: initial points design, search space design, surrogate model, and acquisition function. Then it highlights its methodological aspects and technical details for each approach. Finally, it showcases a wide range of applications and proposes promising future directions.

翻译：包括参数调适、A/B测试和药物设计在内的范围广泛的设计和决策问题,本质上是黑箱优化的例子。贝叶斯优化(BO)是一个强大的工具,可以模拟和优化这种昂贵的“黑箱”功能。然而,在优化之初,香草巴伊西亚优化方法往往由于基于少数试验的模型不准确而出现缓慢的趋同问题。为了解决这一问题,BO社区的研究人员提议将转移学习的精神纳入加速优化进程,这可以从过去的任务(源任务)中借用力量来加速目前的优化问题(目标任务),本调查文件首先总结了从四个角度为巴伊西亚优化转让学习方法:初步点设计、搜索空间设计、代孕模型和获取功能。然后,它强调了每种方法的方法和技术细节。最后,它展示了广泛的应用和技术细节,并提出有希望的未来方向。