Regional Tree Regularization for Interpretability in Black Box Models. - Mike Wu; Sonali Parbhoo; Michael C. Hughes; Ryan Kindle; Leo A. Celi; Maurizio Zazzi; Volker Roth 001; Finale Doshi-Velez (CoRR, 2019)

Regional Tree Regularization for Interpretability in Deep Neural Networks. - Mike Wu; Sonali Parbhoo; Michael C. Hughes; Ryan Kindle; Leo A. Celi; Maurizio Zazzi; Volker Roth 001; Finale Doshi-Velez (AAAI, 2020)

Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment. - Kristine Zhang; Yuanheng Wang; Jianzhun Du; Brian Chu; Leo Anthony Celi; Ryan Kindle; Finale Doshi-Velez (CoRR, 2021)

Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care. - Arne Peine; Ahmed Hallawa; Johannes Bickenbach; Guido Dartmann; Lejla Begic Fazlic; Anke Schmeink; Gerd Ascheid; Christoph Thiemermann; Andreas Schuppert; Ryan Kindle; Leo A. Celi; Gernot Marx; et al. (npj Digit. Medicine, 2021)

An interpretable RL framework for pre-deployment modeling in ICU hypotension management. - Kristine Zhang; Henry Wang; Jianzhun Du; Brian Chu; Aldo Arévalo; Ryan Kindle; Leo Anthony Celi; Finale Doshi-Velez (npj Digit. Medicine, 2022)