65 citations · 107 across the 16 of their papers we have counts for
8 papers · 1 filter
AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
Gabriele Dominici, Pietro Barbiero, Francesco Giannini +2
Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users. Among these techniqes, Concept Bottleneck Mo…
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga +4
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to re…
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
Dario Fenoglio, Gabriele Dominici, Pietro Barbiero +3
Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, t…
Counterfactual Concept Bottleneck Models
Gabriele Dominici, Pietro Barbiero, Francesco Giannini +3
Current deep learning models are not designed to simultaneously address three fundamental questions: predict class labels to solve a given classification task (the "What?"), simula…
Algorithmic Concept-based Explainable Reasoning
Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan +2
Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems. This has numerous benefits, su…
PyTorch, Explain! A Python library for Logic Explained Networks
Pietro Barbiero, Gabriele Ciravegna, Dobrik Georgiev +1
"PyTorch, Explain!" is a Python module integrating a variety of state-of-the-art approaches to provide logic explanations from neural networks. This package focuses on bringing the…
Modeling Generalization in Machine Learning: A Methodological and Computational Study
Pietro Barbiero, Giovanni Squillero, Alberto Tonda
As machine learning becomes more and more available to the general public, theoretical questions are turning into pressing practical issues. Possibly, one of the most relevant conc…
Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms
Pietro Barbiero, Giovanni Squillero, Alberto Tonda
A coreset is a subset of the training set, using which a machine learning algorithm obtains performances similar to what it would deliver if trained over the whole original data. C…