23 citations · 34 across the 4 of their papers we have counts for
5 papers
Machine Learning for Health symposium 2022 -- Extended Abstract track
Antonio Parziale, Monica Agrawal, Shalmali Joshi +4
A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28,…
Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra +4
Modern decision-making systems, from robots to web recommendation engines, are expected to adapt: to user preferences, changing circumstances or even new tasks. Yet, it is still un…
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings
Shengpu Tang, Jenna Wiens
Reinforcement learning (RL) can be used to learn treatment policies and aid decision making in healthcare. However, given the need for generalization over complex state/action spac…
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang, Aditya Modi, Michael W. Sjoding +1
Standard reinforcement learning (RL) aims to find an optimal policy that identifies the best action for each state. However, in healthcare settings, many actions may be near-equiva…
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series
Jeeheh Oh, Jiaxuan Wang, Shengpu Tang +2
Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part…