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Junqi Jiang
Titolo
Citata da
Citata da
Anno
Formalising the Robustness of Counterfactual Explanations for Neural Networks
J Jiang, F Leofante, A Rago, F Toni
AAAI 2023, 2023
172023
Recourse under Model Multiplicity via Argumentative Ensembling
J Jiang, A Rago, F Leofante, F Toni
arXiv preprint arXiv:2312.15097, 2023
32023
Robust Counterfactual Explanations in Machine Learning: A Survey
J Jiang, F Leofante, A Rago, F Toni
arXiv preprint arXiv:2402.01928, 2024
12024
Interval Abstractions for Robust Counterfactual Explanations
J Jiang, F Leofante, A Rago, F Toni
arXiv preprint arXiv:2404.13736, 2024
2024
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
J Jiang, J Lan, F Leofante, A Rago, F Toni
Asian Conference on Machine Learning 222, 582-597, 2024
2024
Should counterfactual explanations always be data instances?
J Jiang, A Rago, F Toni
XLoKR Workshop at KR 2022, 2022
2022
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–6