Shengpu Tang - RankMe

29 papers A* 4Journal 16Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression. CoRR Richard Hou, Shengpu Tang, Wei Jin
2025	—	`ed.`	Conference on Health, Inference, and Learning, UC Berkeley, Berkeley, USA, 25-27 June 2025. CHIL Xuhai Orson Xu, Edward Choi, Pankhuri Singhal, Walter Gerych, Shengpu Tang, Monica Agrawal, Adarsh Subbaswamy, Elena Sizikova, Jessilyn Dunn, Roxana Daneshjou, Tasmie Sarker, Matthew McDermott, Irene Chen
2025	J	`jnl`	Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment. CoRR Yingchuan Sun, Shengpu Tang
2025	J	`jnl`	PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation. CoRR Juntong Ni, Saurabh Kataria, Shengpu Tang, Carl Yang, Xiao Hu, Wei Jin
2025	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference (MLHC 2025), 15-16 August 2025, Mayo Clinic, Rochester, MN, USA MLHC Monica Agrawal, Kaivalya Deshpande, Matthew Engelhard, Shalmali Joshi, Shengpu Tang, Iñigo Urteaga
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`	Slow-Fast Policy Optimization: Reposition-Before-Update for LLM Reasoning. CoRR Ziyan Wang, Zheng Wang, Jie Fu, Xingwei Qu, Qi Cheng, Shengpu Tang, Minjia Zhang, Xiaoming Huo
2025	A*	`conf`	Transforming Healthcare Decision Making Using Artificial Intelligence. AAAI Shengpu Tang
2024	J	`jnl`	CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation. CoRR Aishwarya Mandyam, Shengpu Tang, Jiayu Yao, Jenna Wiens, Barbara E. Engelhardt
2024	—	`—`	Towards Clinically Applicable Reinforcement Learning Shengpu Tang
2023	A*	`conf`	Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. NeurIPS Shengpu Tang, Jenna Wiens
2023	J	`jnl`	Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. CoRR Shengpu Tang, Jenna Wiens
2023	J	`jnl`	Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. CoRR Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens
2023	J	`jnl`	Leveraging Factored Action Spaces for Off-Policy Evaluation. CoRR Aaman Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo
2023	—	`conf`	Machine Learning for Health (ML4H) 2023. ML4H@NeurIPS Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Kristen Severson, Mercy Nyamewaa Asiedu, Serina Chang, Bonaventure F. P. Dossou, Qian Huang, Fahad Kamran, Haoran Zhang, Sujay Nagaraj, Luis Oala, Shan Xu, Chinasa T. Okolo, Helen Zhou, Jessica Dafflon, Caleb Ellington, Sarah Jabbour, Hyewon Jeong, Harry Reyes Nieva, Yuzhe Yang, Ghada Zamzmi, Vishwali Mhasawade, Van Truong, Payal Chandak, Matthew Lee, Peniel N. Argaw, Kyle Heuton, Harvineet Singh, Thomas Hartvigsen
2023	J	`jnl`	Machine Learning for Health symposium 2023 - Findings track. CoRR Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Thomas Hartvigsen, Harvineet Singh
2023	—	`ed.`	Machine Learning for Health, ML4H@NeurIPS 2023, 10 December 2023, New Orleans, Louisiana, USA. ML4H@NeurIPS Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Tom Hartvigsen, Harvineet Singh
2022	A*	`conf`	Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. NeurIPS Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens
2022	—	`conf`	Machine Learning for Health (ML4H) 2022. ML4H@NeurIPS Antonio Parziale, Monica Agrawal, Shengpu Tang, Kristen Severson, Luis Oala, Adarsh Subbaswamy, Sayantan Kumar, Elora D. M. Schörverth, Stefan Hegselmann, Helen Zhou, Ghada Zamzmi, Purity Mugambi, Elena Sizikova, Girmaw Abebe Tadesse, Yuyin Zhou, Taylor W. Killian, Haoran Zhang, Fahad Kamran, Andrea Hobby, Mars Huang, Ahmed M. Alaa, Harvineet Singh, Irene Y. Chen, Shalmali Joshi
2022	J	`jnl`	Machine Learning for Health symposium 2022 - Extended Abstract track. CoRR Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy
2022	—	`ed.`	Machine Learning for Health, ML4H 2022, 28 November 2022, New Orleans, Lousiana, USA & Virtual. ML4H@NeurIPS Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy
2022	J	`jnl`	Towards Data-Driven Offline Simulations for Online Reinforcement Learning. CoRR Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman
2021	—	`conf`	Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. MLHC Shengpu Tang, Jenna Wiens
2021	J	`jnl`	Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. CoRR Shengpu Tang, Jenna Wiens
2020	A*	`conf`	Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. ICML Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens
2020	J	`jnl`	Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. CoRR Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens
2020	J	`jnl`	Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Medical Informatics Assoc. Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, Jenna Wiens
2019	—	`conf`	Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. MLHC Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens
2019	J	`jnl`	Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. CoRR Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens