New Method of Machine Learning with Interpretability Constraints
Published: 05.04.22 — Dr. Michael Mark and Prof. Thomas Weber’s most recent paper, on “Optimal Recovery of Unsecured Debt via Interpretable Machine Learning,” published in Machine Learning with Applications, develops a method to incorporate domain knowledge directly into a reinforcement-learning agent. The new tool is tested on the problem of credit collections, for which an optimal solution is known, so that the augmented learning performance can be compared to an established benchmark. A key observation is that interpretability in the form of monotonicity constraints may be imposed without significant loss in performance. The paper was also co-authored with Prof. Naveed Chehrazi (Olin Business School) and Huanxi Liu who participated in an Excellence Research Internship at EPFL, hosted by the Chair of Operations, Economics and Strategy, while pursuing his bachelor’s degree at UC San Diego.
