Author

R. Rane

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Preprint Aug 2026

ICON decomposition: Multivariate concept auditing reveals shortcuts that neural networks rely on

This work reframe shortcut auditing as a decomposition of the variance of intermediary representations with Independent Canonical cONcepts (ICON), which separates learned shortcuts from merely correlated concepts, which it confirms with independent validation checks.

R. Rane, Marco Simnacher, Manuel Pfeuffer et al. · 0 citations

Preprint Aug 2026

ICON Decomposition: Auditing deep neural networks for shortcuts by decomposing layer-wise representations using concepts

Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Before deployment, models should be audited for reliance on a set of concepts, such as acquisition artifacts or demographics. Current methods, such as linear probes and concept activation vectors, measure reliance by asking...

R. Rane, Marco Simnacher, Manuel Pfeuffer et al. · 0 citations

#machine learning Preprint Oct 2026

Beyond Decodability: Do Acoustic Factors Drive Predictions in Speech-Based Alzheimer's Assessment?

Speech-based Alzheimer's disease (AD) assessments increasingly rely on pretrained self-supervised learning (SSL) models that learn acoustic representations directly from raw audio, exposing the model to recording factors. We ask whether such factors are merely encoded in SSL representations or can systematically alter...

Serli Kopar, Alkis Koudounas, R. Rane et al. · 0 citations

Open access Aug 2026

Evaluating clinical and neuroimaging predictors for cognitive-behavioral therapy outcome in obsessive-compulsive disorder

The findings suggest that robust, individualized brain-based predictions for mental health outcomes remain challenging with the available data and sample size.

Marija Tochadse, Julia Klawohn, Christian Kaufmann et al. · Scientific Reports · 0 citations

Preprint Aug 2026

Controlling for Omitted Variable Bias in Deep Neural Networks

This work introduces an estimation procedure that refits the final layer of a pre-trained network to include covariate effects, and shows how these effects can be orthogonalised with respect to covariates to exclude their mediated effects and that model predictions can be marginalised over the covariate distribution to...

Manuel Pfeuffer, R. Rane, Kerstin Ritter et al. · 0 citations

#artificial intelligence Preprint Aug 2026

ICON decomposition : Auditing deep neural networks with multivariate, variance-based concept-level explanations

CON decomposition is introduced, which quantifies how much of a layer's variance each concept explains given all other concepts and the outcome, and how much none of them explains, yielding layer-comparable, calibrated scores that suppress false positives.

Roshan Prakash, Marco Simnacher, Manuel Pfeuffer et al. · 0 citations