Batch Bayesian optimisation (BO) has shown to be a sample-efficient method of performing optimisation where expensive-to-evaluate objective functions can be queried in parallel. However, current methods do not scale to large batch sizes -- a frequent desideratum in practice (e.g. drug discovery or simulation-based inference). We present a novel algorithm, SOBER, which permits scalable and diversified batch BO with arbitrary acquisition functions, arbitrary input spaces (e.g. graph), and arbitrary kernels. The key to our approach is to reformulate batch selection for BO as a Bayesian quadrature (BQ) problem, which offers computational advantages. This reformulation is beneficial in solving BQ tasks reciprocally, which introduces the exploitative functionality of BO to BQ. We show that SOBER offers substantive performance gains in synthetic and real-world tasks, including drug discovery and simulation-based inference.

翻译：摘要：批量贝叶斯优化已被证明是一种样本高效的优化方法，适用于可并行查询且评估成本高昂的目标函数。然而，现有方法难以扩展到大批量规模——这在实际应用（如药物发现或基于模拟的推断）中常为关键需求。本文提出一种新型算法SOBER，能够实现任意采集函数、任意输入空间（如图结构）及任意核函数的可扩展多样化批量贝叶斯优化。本方法的核心在于将批量贝叶斯优化的选择问题重新表述为贝叶斯求积问题，从而获得计算优势。这种重新表述对互惠解决贝叶斯求积任务同样有益，为贝叶斯求积引入了贝叶斯优化的利用性功能。实验表明，SOBER在合成任务及实际任务（包括药物发现和基于模拟的推断）中均展现出显著的性能提升。