Jean Feng - RankMe

36 papers A* 5A 3Journal 26Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Human-AI Co-design for Clinical Prediction Models. CoRR Jean Feng, Avni Kothari, Patrick Vossler, Andrew Bishara, Lucas Zier, Newton Addo, Aaron Kornblith, Yan Shuo Tan, Chandan Singh
2026	J	`jnl`	More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes. CoRR Venkatesh Sivaraman, Patrick Vossler, Adam Perer, Julian Hong, Jean Feng
2026	A*	`conf`	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. AAAI Avni Kothari, Patrick Vossler, Jean Digitale, Mohammad Forouzannia, Elise Rosenberg, Michele Lee, Jennee Bryant, Melanie F. Molina, James Marks, Lucas Zier, Jean Feng
2025	A*	`conf`	"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. ICML Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C. Hong, Jean Feng
2025	J	`jnl`	"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. CoRR Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C. Hong, Jean Feng
2025	J	`jnl`	Judging LLMs on a Simplex. CoRR Patrick Vossler, Fan Xia, Yifan Mai, Jean Feng
2025	J	`jnl`	Potential for Algorithmic Bias in Clinical Decision Instrument Development. npj Digit. Medicine Jed Keenan Obra, Chandan Singh, Kenshata Watkins, Jean Feng, Ziad Obermeyer, Aaron Kornblith
2025	J	`jnl`	Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control. CoRR Will Y. Zou, Jean Feng, Alexandre Kalimouttou, Jennifer Yuntong Zhang, Christopher W. Seymour, Romain Pirracchio
2025	J	`jnl`	Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025. CoRR Emily Alsentzer, Marie-Laure Charpignon, Bill Chen, Niharika D'Souza, Jason A. Fries, Yixing Jiang, Aparajita Kashyap, Chanwoo Kim, Simon A. 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 C. Weiss, Daniel Wolfe, Emily Wong, Hye Sun Yun, Xiaoman Zhang, Xiao Yu Cindy Zhang, Hayoung Jeong, Kaveri A. Thakoor
2025	J	`jnl`	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 Avni Kothari, Patrick Vossler, Jean Digitale, Mohammad Forouzannia, Elise Rosenberg, Michele Lee, Jennee Bryant, Melanie F. Molina, James Marks, Lucas Zier, Jean Feng
2024	A*	`conf`	A hierarchical decomposition for explaining ML performance discrepancies. NeurIPS Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann, Jean Feng
2024	J	`jnl`	A hierarchical decomposition for explaining ML performance discrepancies. CoRR Jean Feng, Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann
2024	J	`jnl`	Bayesian Concept Bottleneck Models with LLM Priors. CoRR Jean Feng, Avni Kothari, Luke Zier, Chandan Singh, Yan Shuo Tan
2024	—	`conf`	Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. CLeaR Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Anthony Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia
2024	A	`conf`	Is this model reliable for everyone? Testing for strong calibration. AISTATS Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner
2024	A	`conf`	Monitoring machine learning-based risk prediction algorithms in the presence of performativity. AISTATS Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio
2023	J	`jnl`	A Brief Tutorial on Sample Size Calculations for Fairness Audits. CoRR Harvineet Singh, Fan Xia, Mi-Ok Kim, Romain Pirracchio, Rumi Chunara, Jean Feng
2023	J	`jnl`	Author Correction: Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. npj Digit. Medicine 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
2023	J	`jnl`	Is this model reliable for everyone? Testing for strong calibration. CoRR Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner
2023	J	`jnl`	Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. CoRR Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia
2022	J	`jnl`	Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. J. Am. Medical Informatics Assoc. Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio
2022	J	`jnl`	Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. npj Digit. Medicine Jean Feng, Rachael V. Phillips, Ivana Malenica, Andrew Bishara, Alan E. Hubbard, Leo A. Celi, Romain Pirracchio
2022	J	`jnl`	Ensembled sparse-input hierarchical networks for high-dimensional datasets. Stat. Anal. Data Min. Jean Feng, Noah Simon
2022	J	`jnl`	Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions. CoRR Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio
2022	J	`jnl`	Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. npj Digit. Medicine 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
2022	A	`conf`	Sequential algorithmic modification with test data reuse. UAI Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann
2022	J	`jnl`	Sequential algorithmic modification with test data reuse. CoRR Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann
2021	J	`jnl`	Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. CoRR Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio
2021	—	`conf`	Learning to safely approve updates to machine learning algorithms. CHIL Jean Feng
2020	A*	`conf`	Efficient nonparametric statistical inference on population feature importance using Shapley values. ICML Brian D. Williamson, Jean Feng
2020	J	`jnl`	Ensembled sparse-input hierarchical networks for high-dimensional datasets. CoRR Jean Feng, Noah Simon
2020	J	`jnl`	Learning how to approve updates to machine learning algorithms in non-stationary settings. CoRR Jean Feng
2019	J	`jnl`	An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression. CoRR Jean Feng, Noah Simon
2019	J	`jnl`	Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep. CoRR Jean Feng, Scott Emerson, Noah Simon
2019	J	`jnl`	Selective prediction-set models with coverage guarantees. CoRR Jean Feng, Arjun Sondhi, Jessica Perry, Noah Simon
2018	A*	`conf`	Nonparametric variable importance using an augmented neural network with multi-task learning. ICML Jean Feng, Brian D. Williamson, Marco Carone, Noah Simon