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Gabriele Tolomei

2 accepted papers

2025

Natural Language Counterfactual Explanations for Graphs Using Large Language Models

AISTATS 2025poster

Explainable Artificial Intelligence (XAI) has emerged as a critical area of research to unravel the opaque inner logic of (deep) machine learning models. Among the various XAI techniques proposed in the literature, counterfactual explanations stand out as one of the most promising approaches. Howev…

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2022

CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks

AISTATS 2022poster

Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions. Existing methods for interpreting predictions from GNNs have primarily focused on generating subgraphs that are especially relevant for a par…