Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm that, based on the current state, dynamically switches between a sequential policy and a batch policy with variable batch sizes. We provide theoretical justification for our algorithm and present experimental results on eight benchmark BO problems. The results show that our method achieves substantial speedup (up to %78) compared to a pure sequential policy, without suffering any significant performance loss.

研究在 Bayeasian Optimization 中如何使用高斯过程作为后验估计器，并提出了一种混合算法，该算法基于当前状态在顺序策略和批处理策略之间动态切换，批处理策略具有可变的批次大小。本文给出了算法的理论并在八个基准BO问题上展示了实验结果。结果表明，相比纯顺序策略，该方法可以实现大幅度的加速（高达% 78），而不会遭受任何显着的性能损失。

混合批次贝叶斯优化