Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization
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 redunda...
| Authors | Ben Riegler, James Odgers, Vincent Fortuin |
|---|---|
| Primary category | stat.ML |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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