Global Safe Sequential Learning via Efficient Knowledge Transfer

arXiv:2402.14402v1 Announce Type: new
Abstract: Sequential learning methods such as active learning and Bayesian optimization select the most informative data to learn about a task. In many medical or engineering applications, the data selection is constrained by a priori unknown safety conditions. A promissing line of safe learning methods utilize Gaussian processes (GPs) to model the safety probability and perform data selection in areas with high safety confidence. However, accurate safety modeling requires prior knowledge or consumes data. In addition, …

abstract active learning applications arxiv bayesian cs.lg data engineering gaussian processes global knowledge learn line medical optimization processes safety stat.ml transfer type via