See also: Team.
Publications
2026
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Clarifying Uncertainty Quantification in Off-Policy Evaluation: Beyond Effective Sample Sizes, Towards Confidence Intervals
Aditya Dutta, Kaixuan Liu, Shengpu Tang
ICML 2026 “DEMO” Workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning
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Trajectory-Level Model Learning for Off-Policy Evaluation
Kaixuan Liu, Guojun Xiong, Shengpu Tang
In submission
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Flow-Guided Trajectory Diffusion for Off-Policy Evaluation
Kaixuan Liu, Guojun Xiong, Shengpu Tang
In submission
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Autoregressive Diffusion World Models for Off-Policy Evaluation of LLM Agents
Kaixuan Liu, Guojun Xiong, Weinan Zhang, Shengpu Tang
In submission
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Temporal Mammogram Machine Learning for Breast Cancer Risk Prediction
Tina Piltner, Hari Trivedi, Judy Gichoya, Shengpu Tang
In submission
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EPI-SST: Probabilistic Epidemic Forecasting with Latent State-Space Transformers
Yiyun Chen, Zewen Liu, Shengpu Tang, Max SY Lau, Wei Jin
In submission
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Reconciling Set-Valued Policy & Dead-End Discovery in Healthcare Reinforcement Learning: An Empirical Analysis
Victor Li, Sixing Wu, Shengpu Tang
In submission
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EAGLE: Exercise-Adapted blood Glucose Learning Environment for Type 1 Diabetes Blood Glucose Control
Owen Tucker, Michael S. Hughes, Temiloluwa Prioleau, Shengpu Tang
In submission
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Precedence-Aware Resource Allocation: Extending the AUTOC Framework to Multi-Level Treatments
Olin Gilster, Shengpu Tang
In submission
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GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting
Baiying Lu, Zhaohui Liang, Ryan Pontius, Shengpu Tang, Temiloluwa Prioleau
In submission
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Provably Efficient Model-free Representation Learning for Low-Rank Constrained Markov Decision Processes
Kaixuan Liu, Guojun Xiong, Shengpu Tang, Wanyun Si, Jian Li
In submission
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Longitudinal Progression Prediction of Alzheimer’s Disease with Tabular Foundation Model
Yilang Ding, Jiawen Ren, Jiaying Lu, Hyunjung Gloria Kwak, Armin Iraji, Shengpu Tang, Alex Fedorov
In submission
2025
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Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment
Yingchuan Sun, Shengpu Tang
ML4H 2025 - Proceedings Track. Dec 2025.
Also presented at workshops: ARLET @ NeurIPS 2025, TS4H @ NeurIPS 2025, RL4RS @ RLC 2025
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An Adaptive Machine Learning Triage Framework for Predicting Alzheimer’s Disease Progression
Richard Hou, Shengpu Tang*, Wei Jin*
ML4H 2025 - Findings Track. Dec 2025
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Off by a Beat: Temporal Misalignment in Offline RL for Healthcare
Shengpu Tang, Jiayu Yao, Jenna Wiens, Sonali Parbhoo
RLC 2025 Finding the Frame Workshop; RLC 2025 RL4RS Workshop. Aug 2025.
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Reconciling Set-Valued Policy & Dead-End Discovery in RL: An Empirical Analysis
Sixing Wu, Shengpu Tang
RLC 2025 CoCoMARL Workshop; RLC 2025 Finding the Frame Workshop. Aug 2025.
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Between Life and Death: Examining Sparse Reward Designs in Healthcare RL
Yuxuan Shi*, Matthew Lafrance*, Shengpu Tang
RLC 2025 RL4RS Workshop. Aug 2025.
