3 papers
cs.LG2023
Finding Competence Regions in Domain Generalization
Jens Müller, Stefan T. Radev, Robert Schmier +3
We investigate a "learning to reject" framework to address the problem of silent failures in Domain Generalization (DG), where the test distribution differs from the training distr…
cs.LG2022
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data
Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle
Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing flow via…
cs.LG2020
Learning Robust Models Using The Principle of Independent Causal Mechanisms
Jens Müller, Robert Schmier, Lynton Ardizzone +2
Standard supervised learning breaks down under data distribution shift. However, the principle of independent causal mechanisms (ICM, Peters et al. (2017)) can turn this weakness i…