Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization

Understand

Bayesian optimization (BO) is widely used to optimize expensive-to-evaluate black-box functions.BO first builds a surrogate model to represent the objective function and assesses its uncertainty.

• It then decides where to sample by maximizing an acquisition function (AF) based on the surrogate model.
• However, when dealing with high-dimensional problems, finding the global maximum of the AF becomes increasingly challenging.
• In such cases, the initialization of the AF maximizer plays a pivotal role, as an inadequate setup can severely hinder the effectiveness of the AF.