Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes

Understand

In Bayesian Optimization (BO) we study black-box function optimization with noisy point evaluations and Bayesian priors.

• Convergence of BO can be greatly sped up by batching, where multiple evaluations of the black-box function are performed in a single round.
• The main difficulty in this setting is to propose at the same time diverse and informative batches of evaluation points.
• In this work, we introduce DPP-Batch Bayesian Optimization (DPP-BBO), a universal framework for inducing batch diversity in sampling based BO by leveraging the repulsive properties of Determinantal Point Processes (DPP) to naturally diversify the batch sampling procedure.