51 citations · 67 across the 8 of their papers we have counts for
4 papers · 1 filter
Out-of-Distribution Detection using Counterfactual Distance
Maria Stoica, Francesco Leofante, Alessio Lomuscio
Accurate and explainable out-of-distribution (OOD) detection is required to use machine learning systems safely. Previous work has shown that feature distance to decision boundarie…
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
Junqi Jiang, Jianglin Lan, Francesco Leofante +2
Counterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defi…
Formalising the Robustness of Counterfactual Explanations for Neural Networks
Junqi Jiang, Francesco Leofante, Antonio Rago +1
The use of counterfactual explanations (CFXs) is an increasingly popular explanation strategy for machine learning models. However, recent studies have shown that these explanation…
Verification of Neural Networks: Enhancing Scalability through Pruning
Dario Guidotti, Francesco Leofante, Luca Pulina +1
Verification of deep neural networks has witnessed a recent surge of interest, fueled by success stories in diverse domains and by abreast concerns about safety and security in env…