3 citations · 6 across the 3 of their papers we have counts for
5 papers
Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents
Muhang Tian, Sherry Yang
Traditional reinforcement learning (RL) techniques focus on maximizing expected cumulative reward, where each action assumes to take a constant unit of time. However, this assumpti…
Fast and Interpretable Mortality Risk Scores for Critical Care Patients
Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova +4
Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (…
Multi-objective Reinforcement Learning with Nonlinear Preferences: Provable Approximation for Maximizing Expected Scalarized Return
Nianli Peng, Muhang Tian, Brandon Fain
We study multi-objective reinforcement learning with nonlinear preferences over trajectories. That is, we maximize the expected value of a nonlinear function over accumulated rewar…
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models
Muhang Tian, Bernie Chen, Allan Guo +2
Electronic Health Records (EHRs) are rich sources of patient-level data, offering valuable resources for medical data analysis. However, privacy concerns often restrict access to E…
Welfare and Fairness in Multi-objective Reinforcement Learning
Zimeng Fan, Nianli Peng, Muhang Tian +1
We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward…