Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization
This work simplifies asynchronous Bayesian optimization for practitioners by showing that existing complex methods are unnecessary, though it is incremental as it refines prior assumptions.
The paper challenges the assumption that standard acquisition functions cause redundant queries in asynchronous Bayesian optimization, showing that they achieve theoretical guarantees similar to sequential Thompson sampling and match or outperform specialized methods in experiments.
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.