Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. - Neil Jethani; Mukund Sudarshan; Yindalon Aphinyanaphongs; Rajesh Ranganath (AISTATS, 2021)

FastSHAP: Real-Time Shapley Value Estimation. - Neil Jethani; Mukund Sudarshan; Ian Connick Covert; Su-In Lee; Rajesh Ranganath (ICLR, 2022)

New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography. - Neil Jethani; Aahlad Manas Puli; Hao Zhang; Leonid Garber; Lior Jankelson; Yindalon Aphinyanaphongs; Rajesh Ranganath (CoRR, 2022)

Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. - Neil Jethani; Adriel Saporta; Rajesh Ranganath (AISTATS, 2023)

A dynamic risk score for early prediction of cardiogenic shock using machine learning. - Yuxuan Hu; Albert Lui; Mark Goldstein; Mukund Sudarshan; Andrea Tinsay; Cindy Tsui; Samuel Maidman; John Medamana; Neil Jethani; Aahlad Manas Puli; Vuthy Nguy; Yindalon Aphinyanaphongs; et al. (CoRR, 2023)