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We propose an inherently interpretable model that combines the feature extraction capabilities of deep neural networks with advantages of sparse linear models in interpretability.

We demonstrate the limitations of OOD detection for subgroup shifts, i.e. shifts within the support of the original distribution. We show how such shifts can be detected on a population level instead and establish a baseline on histopathology images.