This analysis delves into the practical safety implications of using heuristics in frequentist uncertainty bounds within SafeOpt algorithms. It discusses the issues with heuristics, demonstrates safety problems through experiments, and proposes Real-β-SafeOpt as a solution. The study also introduces Lipschitz-only Safe Bayesian Optimization (LoSBO) to address assumptions related to RKHS norms and safety guarantees.
Introduction
Background
Problem Setting and Objectives
Real-β-SafeOpt
Lipschitz-only Safe Bayesian Optimization (LoSBO)
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by Chri... às arxiv.org 03-20-2024
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