48 citations · 55 across the 8 of their papers we have counts for
6 papers · 1 filter
To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera +3
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machi…
One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption
Maciej Krzysztof Zuziak, Roberto Pellungrini, Salvatore Rinzivillo
Federated Learning (FL) is a widespread and well-adopted paradigm of decentralised learning that allows training one model from multiple sources without the need to transfer data b…
One-Shot Clustering for Federated Learning
Maciej Krzysztof Zuziak, Roberto Pellungrini, Salvatore Rinzivillo
Federated Learning (FL) is a widespread and well adopted paradigm of decentralized learning that allows training one model from multiple sources without the need to directly transf…
Interpretable and Fair Mechanisms for Abstaining Classifiers
Daphne Lenders, Andrea Pugnana, Roberto Pellungrini +3
Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the cl…
Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
Andrea Pugnana, Riccardo Massidda, Francesco Giannini +6
Concept Bottleneck Models (CBMs) are machine learning models that improve interpretability by grounding their predictions on human-understandable concepts, allowing for targeted in…
Mathematical Foundation of Interpretable Equivariant Surrogate Models
Jacopo Joy Colombini, Filippo Bonchi, Francesco Giannini +3
This paper introduces a rigorous mathematical framework for neural network explainability, and more broadly for the explainability of equivariant operators called Group Equivariant…