Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization

2026-03-13

AI Summary

This work challenges the prevailing assumption that standard acquisition functions—such as Upper Confidence Bound (UCB)—in asynchronous Bayesian optimization inherently lead to redundant queries. Through rigorous theoretical analysis and empirical evaluation, the authors demonstrate that, when intermediate posterior updates are properly accounted for, these standard acquisition functions naturally avoid excessive resampling without requiring additional diversity-enforcing mechanisms. Moreover, the study reveals that explicitly imposing diversity penalties can inadvertently induce over-exploration, degrading performance. Extensive experiments on both synthetic benchmarks and real-world tasks show that the standard approach not only matches but often surpasses the performance of more complex algorithms specifically designed for asynchronous settings, thereby questioning the necessity of specialized diversity strategies in this context.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• asynchronous Bayesian optimization
• acquisition functions
• redundant queries
• diversity enforcement
• posterior updates

Innovation

• Asynchronous Bayesian Optimization
• Standard Acquisition Functions
• Intermediate Posterior Updates
• Redundancy Avoidance
• Over-exploration