3 papers
cs.LG2026
Mixture of Concept Bottleneck Experts
Francesco De Santis, Gabriele Ciravegna, Giovanni De Felice +7
Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor…
cs.LG2026
Federated Concept-Based Models: Interpretable models with distributed supervision
Dario Fenoglio, Arianna Casanova, Francesco De Santis +6
Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely a…
cs.LG2026
Enhanced Data-Driven Product Development via Gradient Based Optimization and Conformalized Monte Carlo Dropout Uncertainty Estimation
Andrea Thomas Nava, Lijo Johny, Fabio Azzalini +2
Data-Driven Product Development (DDPD) leverages data to learn the relationship between product design specifications and resulting properties. To discover improved designs, we tra…