Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization

2026-03-17 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

This paper challenges the prevailing "Hypothesis 1" in asynchronous Bayesian optimization (BO), which posits that standard acquisition functions lead to redundant queries and necessitate complex diversity-enforcing solutions. The authors demonstrate that standard methods like Upper Confidence Bound (UCB) achieve theoretical guarantees comparable to sequential Thompson sampling, attributing this to the often-neglected intermediate posterior updates of the Gaussian Process surrogate model. Extensive experiments across synthetic, real-world, and hyperparameter tuning tasks consistently show that simple standard acquisition functions match or outperform purpose-built asynchronous methods. Further analysis reveals that diversity-enforcing strategies can lead to inefficient over-exploration, whereas standard approaches naturally balance exploration and exploitation, making them sufficient for asynchronous BO. This work adds to a growing body of research suggesting that simpler approaches often perform surprisingly well in BO.

Key takeaway

Standard acquisition functions (UCB, LogEI) are sufficient for asynchronous Bayesian Optimization, disproving the common belief that explicit diversity enforcement is required. Theoretically, asynchronous UCB matches sequential Thompson sampling's regret bounds, while empirically outperforming complex purpose-built methods across synthetic, real-world, and hyperparameter tuning tasks. This allows practitioners to simplify asynchronous BO, avoid over-exploration, and achieve more efficient black-box optimization, especially with increased parallel workers.

Topics

• Asynchronous Bayesian Optimization
• Acquisition Functions
• Gaussian Processes
• Hyperparameter Optimization
• Exploration-Exploitation

Code references

• fortuinlab/SAIS-BO
• georgedeath/aegis
• yucenli/bnn-bo
• QUVA-Lab/COMBO

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.