Mukund Sudarshan

According to our database, Mukund Sudarshan authored at least 9 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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, Nicholas Kiefer, Nathaniel Smilowitz, James Horowitz, Tania Ahuja, Glenn I Fishman, Judith Hochman, Stuart Katz, Samuel Bernard, Rajesh Ranganath

CoRR, 2023

On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease.

Raghav Singhal, Mukund Sudarshan, Anish Mahishi, Sri Kaushik, Luke Ginocchio, Angela Tong, Hersh Chandarana, Daniel K. Sodickson, Rajesh Ranganath, Sumit Chopra

CoRR, 2023

DIET: Conditional independence testing with marginal dependence measures of residual information.

Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

On deep learning tools for scientific discovery in healthcare

Mukund Sudarshan

PhD thesis, 2022

FastSHAP: Real-Time Shapley Value Estimation.

Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

CONTRA: Contrarian statistics for controlled variable selection.

Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.

Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients.

Narges Razavian, Vincent J. Major, Mukund Sudarshan, Jesse Burk-Rafel, Peter Stella, Hardev Randhawa, Seda Bilaloglu, Ji Chen, Vuthy Nguy, Walter Wang, Hao Zhang, Ilan Reinstein, David Kudlowitz, Cameron Zenger, Meng Cao, Ruina Zhang, Siddhant Dogra, Keerthi B. Harish, Brian Bosworth, Fritz Francois, Leora I. Horwitz, Rajesh Ranganath, Jonathan S. Austrian, Yindalon Aphinyanaphongs

npj Digit. Medicine, 2020

Deep Direct Likelihood Knockoffs.

Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020