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20232026
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stat.ML2026

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…

cs.LG2026

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…

stat.ME2026

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…

stat.ME2026

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…

cs.CV2026

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…