在黑盒优化中建立信任：可解释性的综合框架
Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability
AI总结：
提出一套模型无关的指标IEMSO，通过采样核心、批次属性、优化过程和特征重要性四类指标，增强代理优化方法的透明性和可解释性。
摘要：
在受限评估预算内优化昂贵的黑盒函数在许多实际应用中面临重大挑战。代理优化（SO）是一种常见的解决方案，但其由代理模型和采样核心（例如采集函数）的复杂性引入的专有性质往往导致缺乏可解释性和透明度。尽管现有文献主要集中在增强对全局最优的收敛性，但新提出策略的实际解释仍未被充分探索，特别是在批量评估设置中。在本文中，我们提出了代理优化的包容性可解释性指标（IEMSO），这是一组全面的模型无关指标，旨在增强SO方法的透明度、可信度和可解释性。通过这些指标，我们在执行昂贵评估之前和之后为从业者提供中间和事后解释，以建立信任。我们考虑了四类主要指标，每类针对SO过程的特定方面：采样核心指标、批次属性指标、优化过程指标和特征重要性。我们的实验评估证明了所提指标在不同基准上的显著潜力。
英文摘要：
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 explainability and transparency. While existing literature has primarily concentrated on enhancing convergence to global optima, the practical interpretation of newly proposed strategies remains underexplored, especially in batch evaluation settings. In this paper, we propose \emph{Inclusive} Explainability Metrics for Surrogate Optimization (IEMSO), a comprehensive set of model-agnostic metrics designed to enhance the transparency, trustworthiness, and explainability of the SO approaches. Through these metrics, we provide both intermediate and post-hoc explanations to practitioners before and after performing expensive evaluations to gain trust. We consider four primary categories of metrics, each targeting a specific aspect of the SO process: Sampling Core Metrics, Batch Properties Metrics, Optimization Process Metrics, and Feature Importance. Our experimental evaluations demonstrate the significant potential of the proposed metrics across different benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
