A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting

arXiv:2404.12219v1 Announce Type: new
Abstract: Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspecification, and lastly fast massive parallelisation. To address these challenges, we introduce a versatile and modular framework for batch Bayesian optimisation via probabilistic lifting with kernel quadrature, called SOBER, which we present as a Python library based on GPyTorch/BoTorch. Our framework offers the following …

abstract acquisition arxiv bayesian challenges continuous cs.lg cs.na flexibility functions general kernel massive math.na optimisation optimization stat.ml strategy type variables via