Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning

no code implementations • 11 Oct 2023 • Jannik Deuschel, Caleb N. Ellington, Yingtao Luo, Benjamin J. Lengerich, Pascal Friederich, Eric P. Xing

Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models force a tradeoff between accuracy and interpretability, limiting data-driven interpretations of human decision-making processes.

Imitation Learning Multi-Task Learning