15 citations · 50 across the 12 of their papers we have counts for
12 papers
Continuous Time Evidential Distributions for Irregular Time Series
Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen +1
Prevalent in many real-world settings such as healthcare, irregular time series are challenging to formulate predictions from. It is difficult to infer the value of a feature at an…
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning
Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi
In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These s…
Medical Dead-ends and Learning to Identify High-risk States and Treatments
Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian +1
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcemen…
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian +2
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patie…
Multiple Sclerosis Severity Classification From Clinical Text
Alister D Costa, Stefan Denkovski, Michal Malyska +5
Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and r…
Counterfactually Guided Off-policy Transfer in Clinical Settings
Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi
Domain shift, encountered when using a trained model for a new patient population, creates significant challenges for sequential decision making in healthcare since the target doma…