18 citations · 39 across the 9 of their papers we have counts for
9 papers
Leveraging weights signals -- Predicting and improving generalizability in reinforcement learning
Olivier Moulin, Vincent Francois-lavet, Paul Elbers +1
Generalizability of Reinforcement Learning (RL) agents (ability to perform on environments different from the ones they have been trained on) is a key problem as agents have the te…
Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
Robin van de Water, Hendrik Schmidt, Paul Elbers +3
Medical applications of machine learning (ML) have experienced a surge in popularity in recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance…
Improving generalization in reinforcement learning through forked agents
Olivier Moulin, Vincent Francois-Lavet, Mark Hoogendoorn
An eco-system of agents each having their own policy with some, but limited, generalizability has proven to be a reliable approach to increase generalization across procedurally ge…
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents
Olivier Moulin, Vincent Francois-Lavet, Paul Elbers +1
Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typical over-fitting on the training environment. RL agents are often capable of so…
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation
Karina Zadorozhny, Patrick Thoral, Paul Elbers +1
Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. Despite a growing number of un…
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients
Adam Izdebski, Patrick J. Thoral, Robbert C. A. Lalisang +43
Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The conseque…
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
Zhaozhi Qian, William R. Zame, Lucas M. Fleuren +2
Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental problem in many areas. Pure Machine Learning (ML) approaches often fail in the small sample…
Hide-and-Seek Privacy Challenge
James Jordon, Daniel Jarrett, Jinsung Yoon +7
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing. Due to the high dimensionality of clinical time series, adequate de-identifi…
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
David Ruhe, Giovanni Cinà, Michele Tonutti +2
The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learni…