3 papers
cs.LG2026
Efficient Credal Prediction through Decalibration
Paul Hofman, Timo Löhr, Maximilian Muschalik +2
A reliable representation of uncertainty is essential for the application of modern machine learning methods in safety-critical settings. In this regard, the use of credal sets (i.…
stat.ML2025
Credal Prediction based on Relative Likelihood
Timo Löhr, Paul Hofman, Felix Mohr +1
Predictions in the form of sets of probability distributions, so-called credal sets, provide a suitable means to represent a learner's epistemic uncertainty. In this paper, we prop…
cs.CV2020
Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns
Hongwei Li, Timo Loehr, Anjany Sekuboyina +3
In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying…