Batched Bayesian optimization by maximizing the probability of including the optimum

Abstract

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 is competitive with and complements other state-of-the-art methods in batched Bayesian optimization.

References in corpus (11)

• Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (2001)
• Accelerating high-throughput virtual screening through molecular pool-based active learning
• Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
• Unexpected Improvements to Expected Improvement for Bayesian Optimization
• Gaussian Probabilities and Expectation Propagation
• The reparameterization trick for acquisition functions
• Neural Thompson Sampling
• High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
• Efficient and Scalable Batch Bayesian Optimization Using K-Means
• TS-RSR: A provably efficient approach for batch Bayesian Optimization
• LITE: Efficiently Estimating Gaussian Probability of Maximality