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cs.LG2026
Counterfactual Explanations for Hypergraph Neural Networks
Fabiano Veglianti, Lorenzo Antonelli, Gabriele Tolomei
Hypergraph neural networks (HGNNs) effectively model higher-order interactions in many real-world systems but remain difficult to interpret, limiting their deployment in high-stake…
cs.LG2025
Generalizability vs. Counterfactual Explainability Trade-Off
Fabiano Veglianti, Flavio Giorgi, Fabrizio Silvestri +1
In this work, we investigate the relationship between model generalization and counterfactual explainability in supervised learning. We introduce the notion of -valid…
cs.LG2025
Countering Overfitting with Counterfactual Examples
Flavio Giorgi, Fabiano Veglianti, Fabrizio Silvestri +1
Overfitting is a well-known issue in machine learning that occurs when a model struggles to generalize its predictions to new, unseen data beyond the scope of its training set. Tra…