DisCoMBO：通过分布一致性引导专家参与的黑盒优化

中文摘要 AI 辅助

序列模型优化（SMBO）传统上依赖贝叶斯或集成代理进行不确定性量化。尽管历史上被视为完全数据驱动，SMBO日益整合外部领域专家知识以加速发现。为了克服标准采集函数重加权的不透明指导及降低的整合保真度，概率电路（PCs）作为一种生成式代理替代方案出现，通过条件采样实现直接知识注入。然而，这些生成式方法缺乏严格优化所需的正式探索-利用语义。我们引入了分布一致性得分（DisCo），一种新颖的度量，将PC的灵活性和效率与正式的不确定性框架统一起来。DisCo提供了一种有界的、归一化到[0,1]的模型“惊讶度”度量，该度量（1）恢复了与基于核的不确定性（如高斯过程）相当的性质，同时保持线性时间推断，并且（2）能够准确评估外部知识与模型证据的一致性。随后，我们提出了DisCoMBO，一个利用这些性质进行稳健、知识感知优化的框架。我们证明了DisCoMBO是一个零遗憾算法，并在来自AutoML、材料优化和风电场优化的多个基准上展示了其有效性。

英文摘要

Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically treated as fully data-driven, SMBO increasingly integrates external domain expertise to accelerate discovery. To overcome the opaque guidance and diminished integration fidelity of standard acquisition re-weighting, Probabilistic Circuits (PCs) have emerged as a generative surrogate alternative, enabling direct knowledge injection via conditional sampling. However, these generative routines lack the formal exploration-exploitation semantics required for rigorous optimization. We introduce the Distributional Conformance Score (DisCo), a novel metric that unifies the flexibility and efficiency of PCs with a formal uncertainty framework. DisCo provides a bounded, $[0, 1]$-normalized measure of model "surprise" that (1) recovers properties comparable to kernel-based uncertainty known from, e.g., Gaussian Processes, while maintaining linear-time inference, and (2) enables accurate assessment of conformance of external knowledge w.r.t. model evidence. We then present DisCoMBO, a framework leveraging these properties for robust, knowledge-aware optimization. We prove that DisCoMBO is a zero-regret algorithm and demonstrate its effectiveness across diverse benchmarks from AutoML, material optimization, and wind park optimization.

发表机构

• TU Darmstadt（达姆施塔特工业大学）
• DFKI（德国人工智能研究中心）

机构由 AI 辅助整理，请以论文原文为准。