NIPS Proceedingsβ

Bayesian Optimization with Exponential Convergence

Part of: Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Conference Event Type: Poster


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.