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[2602.20152] Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data
Summary
The paper introduces Behavior Learning (BL), a machine learning framework that learns interpretable optimization structures from data, enhancing predictive performance and scalability across various scientific domains.
Why It Matters
Behavior Learning (BL) represents a significant advancement in machine learning by integrating interpretability and scalability into optimization problems. Its applicability across diverse scientific fields underscores its potential to improve decision-making and model understanding in complex systems.
Key Takeaways
- BL framework learns interpretable optimization structures from data.
- It supports both single and hierarchical optimization problems.
- Demonstrates strong predictive performance and scalability to high-dimensional data.
- Integrates behavioral science principles into machine learning.
- The smooth variant (IBL) ensures identifiability of models.
Computer Science > Machine Learning arXiv:2602.20152 (cs) [Submitted on 23 Feb 2026] Title:Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data Authors:Zhenyao Ma, Yue Liang, Dongxu Li View a PDF of the paper titled Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data, by Zhenyao Ma and 2 other authors View PDF HTML (experimental) Abstract: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...