Accelerated Gamut Discovery via Massive Parallelization

摘要

This paper presents a scalable framework for efficiently discovering the performance gamut of different processes. Gamut boundaries comprise the set of highest-performing solutions within a design space. While sampling methods are often inefficient or prone to premature convergence, Bayesian optimization struggles with taking advantage of existing large-scale parallel computation or experimentation. To address these challenges, we utilize Bayesian neural networks as scalable surrogates for performance prediction and uncertainty estimation. We further introduce a novel acquisition function that combines the diversity-driven exploration of stochastic optimization with the information-efficient exploitation of Bayesian optimization. This enables generating large, high-quality batches of samples. Our approach leverages large batch sizes to reduce the number of iterations needed for optimization. We demonstrate its effectiveness on real-world engineering and robotic problems, achieving faster and more extensive discovery of the performance gamut. Code and data are available at https://gitlab.mpi-klsb.mpg.de/nansari/lbn_mobo.