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ORCIDAmr Alkhatib
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2020 – today
- 2025
[b1]Amr Alkhatib:
Addressing Shortcomings of Explainable Machine Learning Methods. KTH Royal Institute of Technology, Sweden, 2025- 2024
- [c8]Amr Alkhatib
, Henrik Boström:
Interpretable Graph Neural Networks for Heterogeneous Tabular Data. DS 2024: 310-324 - [c7]Amr Alkhatib, Sofiane Ennadir, Henrik Boström, Michalis Vazirgiannis:
Interpretable Graph Neural Networks for Tabular Data. ECAI 2024: 1848-1855 - [c6]Amr Alkhatib, Henrik Boström:
Fast Approximation of Shapley Values with Limited Data. SCAI 2024: 95-100 - [i4]Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis:
Explaining Predictions by Characteristic Rules. CoRR abs/2405.21003 (2024) - [i3]Amr Alkhatib, Henrik Boström:
Interpretable Graph Neural Networks for Heterogeneous Tabular Data. CoRR abs/2408.07661 (2024) - 2023
- [c5]Sofiane Ennadir, Amr Alkhatib, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström:
UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks. COMPLEX NETWORKS (1) 2023: 100-111 - [c4]Sofiane Ennadir, Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis:
Conformalized Adversarial Attack Detection for Graph Neural Networks. COPA 2023: 311-323 - [c3]Amr Alkhatib, Henrik Boström, Sofiane Ennadir, Ulf Johansson:
Approximating Score-based Explanation Techniques Using Conformal Regression. COPA 2023: 450-469 - [i2]Amr Alkhatib, Sofiane Ennadir, Henrik Boström, Michalis Vazirgiannis:
Interpretable Graph Neural Networks for Tabular Data. CoRR abs/2308.08945 (2023) - [i1]Amr Alkhatib, Henrik Boström, Sofiane Ennadir, Ulf Johansson:
Approximating Score-based Explanation Techniques Using Conformal Regression. CoRR abs/2308.11975 (2023) - 2022
- [c2]Amr Alkhatib, Henrik Boström, Ulf Johansson:
Assessing Explanation Quality by Venn Prediction. COPA 2022: 42-54 - [c1]Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis:
Explaining Predictions by Characteristic Rules. ECML/PKDD (1) 2022: 389-403
Coauthor Index
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