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2020 – today
- 2025
[c9]Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C. Hong, Jean Feng:
"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. ICML 2025- [i18]Patrick Vossler, Fan Xia, Yifan Mai, Jean Feng:
Judging LLMs on a Simplex. CoRR abs/2505.21972 (2025) - [i17]Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C. Hong, Jean Feng:
"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. CoRR abs/2506.00756 (2025) - [i16]Avni Kothari, Patrick Vossler, Jean Digitale, Mohammad Forouzannia, Elise Rosenberg, Michele Lee, Jennee Bryant, Melanie Molina, James Marks, Lucas Zier, Jean Feng:
When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital. CoRR abs/2508.08504 (2025) - [i15]Will Y. Zou, Jean Feng, Alexandre Kalimouttou, Jennifer Yuntong Zhang, Christopher W. Seymour, Romain Pirracchio:
Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control. CoRR abs/2510.01508 (2025) - [i14]Emily Alsentzer, Marie-Laure Charpignon, Bill Chen, Niharika D'Souza, Jason A. Fries, Yixing Jiang, Aparajita Kashyap, Chanwoo Kim, Simon Lee, Aishwarya Mandyam, Ashery Christopher Mbilinyi, Nikita Mehandru, Nitish Nagesh, Brighton Nuwagira, Emma Pierson, Arvind Pillai, Akane Sano, Tanveer F. Syeda-Mahmood, Shashank Yadav, Elias Adhanom, Muhammad Umar Afza, Amelia Archer, Suhana Bedi, Vasiliki Bikia, Trenton Chang, George H. Chen, Winston Chen, Erica Chiang, Edward Choi, Octavia Ciora, Paz Dozie-Nnamah, Shaza Elsharief, Matthew Engelhard, Ali Eshragh, Jean Feng, Josh Fessel, Scott L. Fleming, Kei Sen Fong, Thomas Frost, Soham Gadgil, Judy Gichoya, Leeor Hershkovich, Sujeong Im, Bhavya Jain, Vincent Jeanselme, Furong Jia, Qixuan Jin, Yuxuan Jin, Daniel Kapash, Geetika Kapoor, Behdokht Kiafar, Matthias Kleiner, Stefan Kraft, Annika Kumar, Daeun Kyung, Zhongyuan Liang, Joanna Lin, Qianchu Liu, Chang Liu, Hongzhou Luan, Chris Lunt, Leopoldo Julian Lechuga López, Matthew B. A. McDermott, Shahriar Noroozizadeh, Connor O'Brien, YongKyung Oh, Mixail Ota, Stephen Pfohl, Meagan Pi, Tanmoy Sarkar Pias, Emma Rocheteau, Avishaan Sethi, Toru Shirakawa, Anita Silver, Neha Simha, Kamile Stankeviciute, Max Sunog, Peter Szolovits, Shengpu Tang, Jialu Tang, Aaron Tierney, John Valdovinos, Byron Wallace, Will Ke Wang, Peter Washington, Jeremy Weiss, Daniel Wolfe, Emily Wong, Hye Sun Yun, Xiaoman Zhang, Xiao Yu Cindy Zhang, Hayoung Jeong, Kaveri A. Thakoor:
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025. CoRR abs/2510.15217 (2025) - 2024
- [c8]Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner:
Is this model reliable for everyone? Testing for strong calibration. AISTATS 2024: 181-189 - [c7]Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio:
Monitoring machine learning-based risk prediction algorithms in the presence of performativity. AISTATS 2024: 919-927 - [c6]Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Anthony Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia:
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. CLeaR 2024: 587-608 - [c5]Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann, Jean Feng:
A hierarchical decomposition for explaining ML performance discrepancies. NeurIPS 2024 - [i13]Jean Feng, Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann:
A hierarchical decomposition for explaining ML performance discrepancies. CoRR abs/2402.14254 (2024) - [i12]Jean Feng, Avni Kothari, Luke Zier, Chandan Singh, Yan Shuo Tan:
Bayesian Concept Bottleneck Models with LLM Priors. CoRR abs/2410.15555 (2024) - 2023
- [j5]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:
Author Correction: Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. npj Digit. Medicine 6 (2023) - [i11]Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner:
Is this model reliable for everyone? Testing for strong calibration. CoRR abs/2307.15247 (2023) - [i10]Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia:
Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. CoRR abs/2311.11463 (2023) - [i9]Harvineet Singh, Fan Xia, Mi-Ok Kim, Romain Pirracchio, Rumi Chunara, Jean Feng:
A Brief Tutorial on Sample Size Calculations for Fairness Audits. CoRR abs/2312.04745 (2023) - 2022
- [j4]Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio:
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. J. Am. Medical Informatics Assoc. 29(5): 841-852 (2022) - [j3]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:
Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. npj Digit. Medicine 5 (2022) - [j2]Jean Feng, Rachael V. Phillips, Ivana Malenica, Andrew Bishara, Alan E. Hubbard, Leo A. Celi, Romain Pirracchio:
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. npj Digit. Medicine 5 (2022) - [j1]Jean Feng, Noah Simon:
Ensembled sparse-input hierarchical networks for high-dimensional datasets. Stat. Anal. Data Min. 15(6): 736-750 (2022) - [c4]Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann:
Sequential algorithmic modification with test data reuse. UAI 2022: 674-684 - [i8]Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann:
Sequential algorithmic modification with test data reuse. CoRR abs/2203.11377 (2022) - [i7]Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio:
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions. CoRR abs/2211.09781 (2022) - 2021
- [c3]Jean Feng:
Learning to safely approve updates to machine learning algorithms. CHIL 2021: 164-173 - [i6]Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio:
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. CoRR abs/2110.06866 (2021) - 2020
- [c2]Brian D. Williamson, Jean Feng:
Efficient nonparametric statistical inference on population feature importance using Shapley values. ICML 2020: 10282-10291 - [i5]Jean Feng, Noah Simon:
Ensembled sparse-input hierarchical networks for high-dimensional datasets. CoRR abs/2005.04834 (2020) - [i4]Jean Feng:
Learning how to approve updates to machine learning algorithms in non-stationary settings. CoRR abs/2012.07278 (2020)
2010 – 2019
- 2019
- [i3]Jean Feng, Noah Simon:
An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression. CoRR abs/1903.12297 (2019) - [i2]Jean Feng, Arjun Sondhi, Jessica Perry, Noah Simon:
Selective prediction-set models with coverage guarantees. CoRR abs/1906.05473 (2019) - [i1]Jean Feng, Scott Emerson, Noah Simon:
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep. CoRR abs/1912.12413 (2019) - 2018
- [c1]Jean Feng, Brian D. Williamson, Marco Carone, Noah Simon:
Nonparametric variable importance using an augmented neural network with multi-task learning. ICML 2018: 1495-1504
Coauthor Index
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