On Safety in Safe Bayesian Optimization

arXiv:2403.12948v1 Announce Type: new
Abstract: Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimization (BO) is used for this. Due to the safety critical nature of these applications, it is of utmost importance that theoretical safety guarantees for these algorithms translate into the real world. In this work, we investigate three safety-related issues of the popular class of SafeOpt-type algorithms. First, these algorithms critically rely …

abstract applications arxiv bayesian biomedical biomedical engineering constraints cs.lg engineering function importance nature optimization robotics safety stat.ml type