Jean Feng

According to our database, Jean Feng authored at least 18 papers between 2018 and 2024.

Collaborative distances:

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

2024

A hierarchical decomposition for explaining ML performance discrepancies.

Jean Feng, Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann

CoRR, 2024

2023

Author Correction: Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

Andre Esteva, Jean Feng, Douwe van der Wal, Shih-Cheng Huang, Jeffry P. Simko, Sandy Devries, Emmalyn Chen, Edward M. Schaeffer, Todd M. Morgan, Yilun Sun, Amirata Ghorbani, Nikhil Naik, Dhruv Nathawani, Richard Socher, Jeff M. Michalski, Mack Roach, Thomas M. Pisansky, Jedidiah M. Monson, Farah Naz, James Wallace, Michelle J. Ferguson, Jean-Paul Bahary, James Zou, Matthew P. Lungren, Serena Yeung, Ashley E. Ross, Michael J. Kucharczyk, Luis Souhami, Leslie Ballas, Christopher A. Peters, Sandy Liu, Alexander G. Balogh, Pamela D. Randolph-Jackson, David L. Schwartz, Michael R. Girvigian, Naoyuki G. Saito, Adam Raben, Rachel A. Rabinovitch, Khalil Katato, Howard M. Sandler, Phuoc T. Tran, Daniel E. Spratt, Stephanie Pugh, Felix Y. Feng, Osama Mohamad

npj Digit. Medicine, 2023

A Brief Tutorial on Sample Size Calculations for Fairness Audits.

Harvineet Singh, Fan Xia, Mi-Ok Kim, Romain Pirracchio, Rumi Chunara, Jean Feng

CoRR, 2023

Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study.

Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia

CoRR, 2023

Is this model reliable for everyone? Testing for strong calibration.

Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner

CoRR, 2023

2022

Ensembled sparse-input hierarchical networks for high-dimensional datasets.

Jean Feng, Noah Simon

Stat. Anal. Data Min., 2022

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.

Jean Feng, Rachael V. Phillips, Ivana Malenica, Andrew Bishara, Alan E. Hubbard, Leo A. Celi, Romain Pirracchio

npj Digit. Medicine, 2022

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

Andre Esteva, Jean Feng, Douwe van der Wal, Shih-Cheng Huang, Jeffry P. Simko, Sandy Devries, Emmalyn Chen, Edward M. Schaeffer, Todd M. Morgan, Yilun Sun, Amirata Ghorbani, Nikhil Naik, Dhruv Nathawani, Richard Socher, Jeff M. Michalski, Mack Roach, Thomas M. Pisansky, Jedidiah M. Monson, Farah Naz, James Wallace, Michelle J. Ferguson, Jean-Paul Bahary, James Zou, Matthew P. Lungren, Serena Yeung, Ashley E. Ross, Michael J. Kucharczyk, Luis Souhami, Leslie Ballas, Christopher A. Peters, Sandy Liu, Alexander G. Balogh, Pamela D. Randolph-Jackson, David L. Schwartz, Michael R. Girvigian, Naoyuki G. Saito, Adam Raben, Rachel A. Rabinovitch, Khalil Katato, Howard M. Sandler, Phuoc T. Tran, Daniel E. Spratt, Stephanie Pugh, Felix Y. Feng, Osama Mohamad

npj Digit. Medicine, 2022

Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees.

Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio

J. Am. Medical Informatics Assoc., 2022

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions.

Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio

CoRR, 2022

Sequential algorithmic modification with test data reuse.

Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

Learning to safely approve updates to machine learning algorithms.

Jean Feng

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020

Learning how to approve updates to machine learning algorithms in non-stationary settings.

Jean Feng

CoRR, 2020

Efficient nonparametric statistical inference on population feature importance using Shapley values.

Brian D. Williamson, Jean Feng

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep.

Jean Feng, Scott Emerson, Noah Simon

CoRR, 2019

Selective prediction-set models with coverage guarantees.

Jean Feng, Arjun Sondhi, Jessica Perry, Noah Simon

CoRR, 2019

An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression.

Jean Feng, Noah Simon

CoRR, 2019

2018

Nonparametric variable importance using an augmented neural network with multi-task learning.

Jean Feng, Brian D. Williamson, Marco Carone, Noah Simon

Proceedings of the 35th International Conference on Machine Learning, 2018