This paper presents a new approach for batch Bayesian Optimization (BO), where the sampling takes place by minimizing a Thompson Sampling approximation of a regret to uncertainty ratio. Our objective is able to coordinate the actions chosen in each batch in a way that minimizes redundancy between points whilst focusing on points with high predictive means or high uncertainty. We provide high-probability theoretical guarantees on the regret of our algorithm. Finally, numerically, we demonstrate that our method attains state-of-the-art performance on a range of nonconvex test functions, where it outperforms several competitive benchmark batch BO algorithms by an order of magnitude on average.

翻译：本文提出了一种批量贝叶斯优化(BO)的新方法，其中采样过程通过最小化遗憾与不确定性比值的汤普森采样近似来实现。我们的目标能够以最小化各点间冗余的方式协调每个批次选择的行动，同时聚焦于具有高预测均值或高不确定性的区域。我们为该算法的遗憾提供了高概率理论保证。最后，数值实验表明，该方法在一系列非凸测试函数上达到了最先进的性能，平均而言，其表现优于多个具有竞争力的基准批量贝叶斯优化算法一个数量级。