Transfer Learning for Bayesian Optimization: A Survey
It provides a comprehensive overview for researchers and practitioners in machine learning and optimization, but it is incremental as it surveys existing methods rather than introducing new ones.
This survey paper addresses the slow convergence of Bayesian optimization (BO) in early stages by summarizing methods that incorporate transfer learning to accelerate optimization, covering approaches like initial points design and surrogate models, and showcasing applications across domains.
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.