102 citations · 364 across the 12 of their papers we have counts for
12 papers
Kernel weighted importance sampling for off-policy evaluation in contextual bandits
Joshua Spear, Matthieu Komorowski, Rebecca Pope +1
This article presents a novel estimator for performing off-policy evaluation using only offline data for contextual bandits. The proposed estimator, Kernel-WIS is demonstrated to b…
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Paul Festor, Giulia Luise, Matthieu Komorowski +1
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, aut…
Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs
Luchen Li, Matthieu Komorowski, Aldo A. Faisal
Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clini…
Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care
Matthieu Komorowski, Leo A. Celi, Omar Badawi +2
In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the…
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
Xuefeng Peng, Yi Ding, David Wihl +6
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the indi…
Model-Based Reinforcement Learning for Sepsis Treatment
Aniruddh Raghu, Matthieu Komorowski, Sumeetpal Singh
Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical…