9 papers
Leveraging the Structure of Medical Data for Improved Representation Learning
Andrea Agostini, Sonia Laguna, Alain Ryser +7
Building generalizable medical AI systems requires pretraining strategies that are data-efficient and domain-aware. Unlike internet-scale corpora, clinical datasets such as MIMIC-C…
Interpretable Reward Modeling with Active Concept Bottlenecks
Sonia Laguna, Katarzyna Kobalczyk, Julia E. Vogt +1
We introduce Concept Bottleneck Reward Models (CB-RM), a reward modeling framework that enables interpretable preference learning through selective concept annotation. Unlike stand…
Interpretable Diffusion Models with B-cos Networks
Nicola Bernold, Moritz Vandenhirtz, Alice Bizeul +1
Text-to-image diffusion models generate images by iteratively denoising random noise, conditioned on a prompt. While these models have enabled impressive progress in image generati…
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
Moritz Vandenhirtz, Julia E. Vogt
Understanding the decision-making process of machine learning models provides valuable insights into the task, the data, and the reasons behind a model's failures. In this work, we…
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes
Marco Voegeli, Sonia Laguna, Heike Leutheuser +3
The dead-in-bed syndrome describes the sudden and unexplained death of young individuals with Type 1 Diabetes (T1D) without prior long-term complications. One leading hypothesis at…
Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach
Mikael Makonnen, Moritz Vandenhirtz, Sonia Laguna +1
Concept Bottleneck Models (CBMs) aim to enhance interpretability by structuring predictions around human-understandable concepts. However, unintended information leakage, where pre…