On Batch Bayesian Optimization
arXiv:1911.01032v112 citations
Originality Incremental advance
AI Analysis
This work addresses optimization problems where feedback is received in batches, which is incremental as it builds on existing Bayesian optimization methods.
The paper tackles batch Bayesian optimization by proposing two algorithms based on Gaussian process upper confidence bound and Thompson sampling, providing frequentist regret guarantees and numerical results.
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.