Draft:Behavior Learning

Behavior Learning
AbbreviationBL
FieldMachine learning
Introduced2026

Behavior Learning (BL) is a machine learning framework for learning interpretable and identifiable optimization structures from data. It was introduced by Zhenyao Ma, Yue Liang, and Dongxu Li in a paper presented at the International Conference on Learning Representations (ICLR) 2026.[1][2][3]

The framework is inspired by behavioral science, particularly utility maximization problems (UMPs), which are commonly used to model decision-making systems. Behavior Learning parameterizes compositional utility functions built from modular components, each of which can be expressed in symbolic form as an optimization problem. The framework supports architectures ranging from single optimization problems to hierarchical compositions representing structured decision systems.[1]

Behavior Learning is related to research areas including inverse optimization, energy-based models, interpretable machine learning, and statistical identifiability. A smooth and monotone variant, referred to as Identifiable Behavior Learning (IBL), provides identifiability guarantees under regularity conditions described in the original publication.[1]

Theoretical properties

The original publication establishes theoretical properties for Behavior Learning and its variant IBL, including universal approximation results and statistical M-estimation analysis. These results characterize the representational capacity and estimation behavior of the framework under specified assumptions.[1]

Implementation

A reference implementation of Behavior Learning is available as open-source software.[4]

References

  1. ^ a b c d Ma, Zhenyao; Liang, Yue; Li, Dongxu (2026). "Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data". arXiv:2602.20152 [cs.LG].
  2. ^ "Behavior Learning (BL)". OpenReview (ICLR 2026). 2026.
  3. ^ "Behavior Learning (BL) Poster". ICLR 2026 Virtual Site. 2026.
  4. ^ "Behavior Learning (BL)". GitHub.

Category:Machine learning Category:Artificial intelligence Category:Optimization algorithms

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