On Batch Bayesian Optimization

11/04/2019

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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.

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On Batch Bayesian Optimization

Local Nonstationarity for Efficient Bayesian Optimization

Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes

Designing over uncertain outcomes with stochastic sampling Bayesian optimization

Hybrid Batch Bayesian Optimization

Regret Bounds for Noise-Free Bayesian Optimization

Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior

Efficient and Scalable Batch Bayesian Optimization Using K-Means

On Batch Bayesian Optimization

Related Research

Local Nonstationarity for Efficient Bayesian Optimization

Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes

Designing over uncertain outcomes with stochastic sampling Bayesian optimization

Hybrid Batch Bayesian Optimization

Regret Bounds for Noise-Free Bayesian Optimization

Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior

Efficient and Scalable Batch Bayesian Optimization Using K-Means