从数据中学习可解释的分层优化结构，提升模型可解释性与预测能力。
Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data
- 用可符号化表示的模块化效用最大化问题构建复合目标函数
- 支持单个到分层优化结构，具备强可解释性与高维数据扩展能力
- 适合需要可解释优化决策的科研与工业场景
受行为科学启发，我们提出行为学习（BL），一种通用机器学习框架，能从数据中学习可解释且可识别的优化结构，涵盖单个优化问题到分层组合。该框架统一了预测性能、内在可解释性与可识别性，适用于涉及优化的众多科学领域。BL通过由内在可解释模块构成的复合效用函数参数化，诱导出用于预测与生成的数据分布。每个模块代表一个效用最大化问题（UMP），是行为科学中的基础范式，也是通用优化框架。BL支持从单个UMP到分层组合的架构，后者用于建模分层优化结构。其平滑单调变体（IBL）保证了可识别性。理论上，我们证明了BL的通用逼近性质，并分析了IBL的M估计性质。实验上，BL展现出强大的预测性能、内在可解释性以及对高维数据的可扩展性。代码：https://github.com/MoonYLiang/Behavior-Learning；安装方式：pip install blnetwork。
原文摘要 · Abstract (English)
Inspired by behavioral science, we propose Behavior Learning (BL), a novel general-purpose machine learning framework that learns interpretable and identifiable optimization structures from data, ranging from single optimization problems to hierarchical compositions. It unifies predictive performance, intrinsic interpretability, and identifiability, with broad applicability to scientific domains involving optimization. BL parameterizes a compositional utility function built from intrinsically interpretable modular blocks, which induces a data distribution for prediction and generation. Each block represents and can be written in symbolic form as a utility maximization problem (UMP), a foundational paradigm in behavioral science and a universal framework of optimization. BL supports architectures ranging from a single UMP to hierarchical compositions, the latter modeling hierarchical optimization structures. Its smooth and monotone variant (IBL) guarantees identifiability. Theoretically, we establish the universal approximation property of BL, and analyze the M-estimation properties of IBL. Empirically, BL demonstrates strong predictive performance, intrinsic interpretability and scalability to high-dimensional data. Code: https://github.com/MoonYLiang/Behavior-Learning ; install via pip install blnetwork.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。