3 papers
stat.ML2026
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli +3
Hazard and survival functions are natural, interpretable targets in time-to-event prediction, but their inherent non-additivity fundamentally limits standard additive explanation m…
cs.CV2025
Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions
Hubert Baniecki, Maximilian Muschalik, Fabian Fumagalli +3
Language-image pre-training (LIP) enables the development of vision-language models capable of zero-shot classification, localization, multimodal retrieval, and semantic understand…
cs.LG2025
Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning
Hubert Baniecki, Przemyslaw Biecek
A common belief is that intrinsically interpretable deep learning models ensure a correct, intuitive understanding of their behavior and offer greater robustness against accidental…