On Batch Bayesian Optimization

Abstract

We present two algorithms for Bayesian optimization in the batch feedback setting, based on Gaussian process upper confidence bound and Thompson sampling approaches, along with frequentist regret guarantees and numerical results.

All of Bayesian Nonparametrics workshop, Neural Information Processing Systems, 2018

References in corpus (4)

• Batch Bayesian Optimization via Local Penalization
• Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
• Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
• Streaming kernel regression with provably adaptive mean, variance, and regularization