提出新方法提升批量贝叶斯优化找最优解效率
Batched Bayesian optimization by maximizing the probability of including the optimum
- 基于最大化包含真最优解的概率设计批量采样策略
- 可解析求解，避免传统方法的组合优化难题
- 适合大规模分子库搜索，与现有方法互补
批量贝叶斯优化可通过高效识别大型化学库中的高性能化合物来加速分子设计。现有批量设计的采集策略通常在探索与利用之间权衡，常需通过贪心构造或多样性启发式近似非可加性批量采集函数。本文提出一种离散优化的采集策略，qPO（多点最优概率），其核心思想是最大化批次包含真实最优解的概率。该概率可表示为单个采集得分之和，从而规避了优化批量采集函数的组合挑战。我们区分了该方法与并行汤普森采样，并讨论其如何隐式捕捉多样性。最后，我们将该方法应用于模型引导的大规模化学库探索，实验证明其在批量贝叶斯优化中具有竞争力且能与其他前沿方法互补。
原文摘要 · Abstract (English)
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation. This often involves optimizing non-additive batch acquisition functions, necessitating approximation via myopic construction and/or diversity heuristics. In this work, we propose an acquisition strategy for discrete optimization that is motivated by pure exploitation, qPO (multipoint Probability of Optimality). qPO maximizes the probability that the batch includes the true optimum, which is expressible as the sum over individual acquisition scores and thereby circumvents the combinatorial challenge of optimizing a batch acquisition function. We differentiate the proposed strategy from parallel Thompson sampling and discuss how it implicitly captures diversity. Finally, we apply our method to the model-guided exploration of large chemical libraries and provide empirical evidence that it is competitive with and complements other state-of-the-art methods in batched Bayesian optimization.
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