简单标准方法在异步贝叶斯优化中表现更优，无需复杂去重设计。
Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization
- 基于后验更新机制，标准方法自然避免重复查询。
- 实验表明标准方法在合成与真实任务上均优于专用异步方法。
- 适合追求高效、轻量级优化的工程场景使用。
异步贝叶斯优化广泛用于独立并行实验且评估时间不一的场景。现有方法认为标准获取函数会导致冗余查询，因而提出复杂方案以强制查询多样性。我们挑战这一基本前提，发现如上限置信区间（UCB）等方法可实现与序列式汤普森采样近乎相同的理论保证。对异步贝叶斯优化的深入分析表明，现有工作忽略了中间后验更新，而我们发现其通常已足够避免冗余查询。进一步研究表明，在繁忙区域施加惩罚的多样性强化方法在异步设置下可能过度探索，反而降低性能。大量实验表明，简单的标准获取函数在合成与真实任务中均能匹配甚至超越专门设计的异步方法。
原文摘要 · Abstract (English)
Asynchronous Bayesian optimization is widely used for gradient-free optimization in domains with independent parallel experiments and varying evaluation times. Existing methods posit that standard acquisitions lead to redundant and repeated queries, proposing complex solutions to enforce diversity in queries. Challenging this fundamental premise, we show that methods, like the Upper Confidence Bound, can in fact achieve theoretical guarantees essentially equivalent to those of sequential Thompson sampling. A conceptual analysis of asynchronous Bayesian optimization reveals that existing works neglect intermediate posterior updates, which we find to be generally sufficient to avoid redundant queries. Further investigation shows that by penalizing busy locations, diversity-enforcing methods can over-explore in asynchronous settings, reducing their performance. Our extensive experiments demonstrate that simple standard acquisition functions match or outperform purpose-built asynchronous methods across synthetic and real-world tasks.
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