Batched Bayesian optimization with correlated candidate uncertainties
<|reference_start|>Batched Bayesian optimization with correlated candidate uncertainties: Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation. This often involves optimizing non-additive batch acquisition functions, necessitating approximation via myopic construction and/or diversity heuristics. In this work, we propose an acquisition strategy for discrete optimization that is motivated by pure exploitation, qPO (multipoint Probability of Optimality). qPO maximizes the probability that the batch includes the true optimum, which is expressible as the sum over individual acquisition scores and thereby circumvents the combinatorial challenge of optimizing a batch acquisition function. We differentiate the proposed strategy from parallel Thompson sampling and discuss how it implicitly captures diversity. Finally, we apply our method to the model-guided exploration of large chemical libraries and provide empirical evidence that it performs better than or on par with state-of-the-art methods in batched Bayesian optimization.<|reference_end|>
@article{fromer2024batched,
    title={Batched Bayesian optimization with correlated candidate uncertainties},
    author={Jenna Fromer, Runzhong Wang, Mrunali Manjrekar, Austin Tripp, Jos'e
  Miguel Hern'andez-Lobato, Connor W. Coley},
    journal={arXiv preprint arXiv:2410.06333},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06333},
    primaryClass={cs.LG stat.ML}
}