Bayesian Optimization with Exponential Convergence
  This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-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 requires access to the delta-cover sampling, which was considered to be impractical. Our approach eliminates both requirements and achieves an exponential convergence rate. 
Kenji Kawaguchi, Leslie Pack Kaelbling, Tom\'as Lozano-P\'erez
stat.ML cs.LG