Bayesian Optimization with Exponential Convergence

This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the δ -cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exponential convergence [ 1] requires access to the δ -cover sampling, which was considered to be impractical [ 1, 2]. Our approach eliminates both requirements and achieves an exponential convergence rate.