Behavior Regularization

Behavior regularization in machine learning aims to constrain model behavior to align with observed data or desired properties, improving safety, robustness, and generalizability. Current research focuses on developing algorithms that effectively incorporate these constraints within various frameworks, including reinforcement learning (e.g., using adaptive regularization or conservative policy iteration) and deep neural networks (e.g., through gradient regularization to enforce behavioral regularities like the law of demand). This work is significant because it addresses critical limitations of complex models, such as unpredictable actions in safety-critical systems or poor generalization in applications like travel demand modeling, leading to more reliable and trustworthy AI systems.