Safe Bayesian Optimization

Safe Bayesian optimization (BO) focuses on efficiently finding optimal parameters for systems with safety constraints, avoiding unsafe regions while maximizing performance. Current research emphasizes developing algorithms that provide probabilistic safety guarantees, often using Gaussian processes or other kernel methods, and addressing challenges like high-dimensionality and the need for fewer assumptions about the system (e.g., relaxing the need for known smoothness bounds). This methodology is proving valuable in diverse applications, including robotics, control systems, and industrial processes, where safe and efficient optimization is crucial.