Imperial College London


Faculty of EngineeringDepartment of Computing

Research Associate



a.rago Website




417Huxley BuildingSouth Kensington Campus






Jiang J, Leofante F, Rago A, et al., Formalising the robustness of counterfactual explanations for neural networks, The 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence

Albini E, Rago A, Baroni P, et al., Descriptive accuracy in explanations: the case of probabilistic classifiers, 15th International Conference on Scalable Uncertainty Management (SUM 2022)

Sukpanichnant P, Rago A, Lertvittayakumjorn P, et al., 2022, Neural QBAFs: explaining neural networks under LRP-based argumentation frameworks, International Conference of the Italian Association for Artificial Intelligence, Springer International Publishing, Pages:429-444, ISSN:0302-9743

Jiang J, Rago A, Toni F, Should counterfactual explanations always be data instances?, XLoKR 2022: The Third Workshop on Explainable Logic-Based Knowledge Representation

Irwin B, Rago A, Toni F, 2022, Argumentative forecasting, AAMAS 2022, ACM, Pages:1636-1638

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