← Search

Arianna Casanova

2 accepted papers

2026

Mixture of Concept Bottleneck Experts

ICML 2026spotlight

Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically fix their task predictor to a single linear or Boolean expression, limiting both predictive accuracy and adaptability to diverse user needs. We propos…

Cited by 0SourceScholar
2025

Causally Reliable Concept Bottleneck Models

NeurIPS 2025poster

Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable variables, facilitating explainability and human interaction. However, these architectures, on par with popular opaque neural models, fail to account for the tr…

Cited by 0SourceScholar