101 citations · 578 across the 13 of their papers we have counts for
6 papers · 1 filter
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Patrick Schwab, Walter Karlen
Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and inte…
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Patrick Schwab, Lorenz Linhardt, Stefan Bauer +2
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as health…
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
Patrick Schwab, Lorenz Linhardt, Walter Karlen
Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Coun…
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patrick Schwab, Emanuela Keller, Carl Muroi +3
Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual a…
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Patrick Schwab, Djordje Miladinovic, Walter Karlen
Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying dat…
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
Patrick Schwab, Gaetano Scebba, Jia Zhang +2
With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for th…