Assessing Explanation Quality by Venn Prediction. - Amr Alkhatib; Henrik Boström; Ulf Johansson (COPA, 2022)

Explaining Predictions by Characteristic Rules. - Amr Alkhatib; Henrik Boström; Michalis Vazirgiannis (ECML/PKDD (1), 2022)

UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks. - Sofiane Ennadir; Amr Alkhatib; Giannis Nikolentzos; Michalis Vazirgiannis; Henrik Boström (COMPLEX NETWORKS (1), 2023)

Approximating Score-based Explanation Techniques Using Conformal Regression. - Amr Alkhatib; Henrik Boström; Sofiane Ennadir; Ulf Johansson (COPA, 2023)

Conformalized Adversarial Attack Detection for Graph Neural Networks. - Sofiane Ennadir; Amr Alkhatib; Henrik Boström; Michalis Vazirgiannis (COPA, 2023)

Estimating Quality of Approximated Shapley Values Using Conformal Prediction. - Amr Alkhatib; Henrik Boström; Ulf Johansson (COPA, 2024)

Interpretable Graph Neural Networks for Heterogeneous Tabular Data. - Amr Alkhatib; Henrik Boström (DS (1), 2024)

Interpretable Graph Neural Networks for Tabular Data. - Amr Alkhatib; Sofiane Ennadir; Henrik Boström; Michalis Vazirgiannis (ECAI, 2024)

Fast Approximation of Shapley Values with Limited Data. - Amr Alkhatib; Henrik Boström (SCAI, 2024)

Explaining Representations in Correlation-based Deep Multiview Representation Learning. - Maurice Kuschel; Amr Alkhatib; Tanuj Hasija; Henrik Boström (ICASSP, 2025)

Addressing Shortcomings of Explainable Machine Learning Methods - Amr Alkhatib (2025)

Prediction via Shapley Value Regression. - Amr Alkhatib; Roman Bresson; Henrik Boström; Michalis Vazirgiannis (CoRR, 2025)