A Continuous Relaxation for Discrete Bayesian Optimization

arXiv:2404.17452v1 Announce Type: new
Abstract: To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and optimization can be computationally tractable. We consider in particular the optimization domain where very few observations and strict budgets exist; motivated by optimizing protein sequences for expensive to evaluate bio-chemical properties. The advantages of our approach are two-fold: the problem is treated …

abstract arxiv bayesian challenge continuous cs.lg data domain function inference optimization show stat.ml tractable type