7 papers
Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speeches
Marco Simnacher, Georg Keilbar, Benjamin König +2
Conditional independence tests (CITs) test for conditional dependence between two random objects and given a third random object . Existing CITs have limited applicabili…
ICON decomposition: Multivariate concept auditing reveals shortcuts that neural networks rely on
Roshan Prakash Rane, Marco Simnacher, Manuel Pfeuffer +7
Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Before deployment, models should therefore be audited for reliance on concepts such…
Controlling for Omitted Variable Bias in Deep Neural Networks
Manuel Pfeuffer, Roshan Prakash Rane, Kerstin Ritter +1
Control variables are widely used in statistical modelling to account for omitted variable bias of known confounders. However, they have largely been underexplored in deep learning…
Deep Shape Regression for Planar Curves with Multimodal Covariates
Manuel Pfeuffer, Roshan Prakash Rane, Hadya Yassin +2
The shape of a planar curve is the geometric information that remains once translation, rotation, scale and reparametrisation are removed and is of interest in many health applicat…
Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance
Vanessa Emanuela Guarino, Claudia Winklmayr, Jannik Franzen +7
Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous dri…
Additive Density Regression
Eva-Maria Maier, Alexander Fottner, Sonja Greven +1
We present a structured additive regression approach to model conditional densities given scalar covariates, where only samples of the conditional distributions are observed. This…
Principal component analysis in Bayes spaces for sparsely sampled density functions
Lisa Steyer, Sonja Greven
This paper presents a novel approach to functional principal component analysis (FPCA) in Bayes spaces in the setting where densities are the object of analysis, but only few indiv…