Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability 事件

Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability arXiv:2410.14573v2 Announce Type: replace-cross Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications. Surrogate Optimization (SO) is a common resolution, yet its proprietary nature introduced by the complexity of surrogate models and the sampling core (e.g., acquisition functions) often leads to a lack of explain