Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation

Jiang, J., Lan, J. , Leofante, F., Rago, A. and Toni, F. (2023) Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation. In: 15th Asian Conference on Machine Learning (ACML 2023), Istanbul, Turkey, 11-14 Nov 2023, (Accepted for Publication)

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Item Type:Conference Proceedings
Additional Information:Jiang, Rago and Toni were partially funded by J.P. Morgan and by the Royal Academy of Engineering under the Research Chairs and Senior Research Fellowships scheme. Jianglin Lan is supported by a Leverhulme Trust Early Career Fellowship under Award ECF-2021-517. Leofante is supported by an Imperial College Research Fellowship grant. Rago and Toni were partially funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101020934).
Keywords:Explainable AI, counterfactual explanations, robustness of explanations.
Status:Accepted for Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lan, Dr Jianglin
Authors: Jiang, J., Lan, J., Leofante, F., Rago, A., and Toni, F.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
314249Decarbonising Machine Learning for Safe and Robust Autonomous SystemsJianglin LanLeverhulme Trust (LEVERHUL)ECF-2021-517ENG - Autonomous Systems & Connectivity