Batch Bayesian Optimization

Batch Bayesian Optimization (BBO) aims to efficiently find the optimal settings of a system by evaluating multiple settings simultaneously, thereby reducing the overall cost of experimentation. Current research focuses on improving the selection of these batches, employing techniques like novel acquisition functions (e.g., minimizing variance or maximizing expected improvement), Thompson sampling, and ensemble methods to balance exploration and exploitation. These advancements are impacting diverse fields, from materials science (e.g., protein design) and manufacturing to experimental design, by enabling more efficient and effective optimization of complex, expensive-to-evaluate processes.