18 citations · 37 across the 12 of their papers we have counts for
6 papers · 1 filter
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series
Rita Kuznetsova, Alizée Pace, Manuel Burger +2
Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in prob…
Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions
Samyak Jain, Manuel Burger, Gunnar Rätsch +1
Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions. Recent deep learning…
Language Model Training Paradigms for Clinical Feature Embeddings
Yurong Hu, Manuel Burger, Gunnar Rätsch +1
In research areas with scarce data, representation learning plays a significant role. This work aims to enhance representation learning for clinical time series by deriving univers…
Multi-modal Graph Learning over UMLS Knowledge Graphs
Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) fo…
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk +2
Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace…
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace, Hugo Yèche, Bernhard Schölkopf +2
A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the obse…