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Artelt, André, et al. “‘I Do Not Know! But Why?’ — Local Model-Agnostic Example-Based Explanations of Reject.”10.1016/j.neucom.2023.126722__Neurocomputing, vol. 558, 126722, Elsevier BV, 2023, doi:10.1016/j.neucom.2023.126722.
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Muschalik, Maximilian, et al. “ISAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.”10.1007/978-3-031-43418-1_26__Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-43418-1_26.
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Muschalik, Maximilian, et al. “IPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.”10.1007/978-3-031-44064-9_11__Communications in Computer and Information Science, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-44064-9_11.
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Fumagalli, Fabian, et al. “Incremental Permutation Feature Importance (IPFI): Towards Online Explanations on Data Streams.”10.1007/s10994-023-06385-y__Machine Learning, vol. 112, no. 12, Springer Science and Business Media LLC, 2023, pp. 4863–903, doi:10.1007/s10994-023-06385-y.
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Fumagalli, Fabian, et al. “On Feature Removal for Explainability in Dynamic Environments.”10.14428/esann/2023.es2023-148__ESANN 2023 Proceedings, i6doc.com publ., 2023, doi:10.14428/esann/2023.es2023-148.
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Fumagalli, Fabian, et al. “SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions.” NeurIPS 2023 - Advances in Neural Information Processing Systems, vol. 36, Curran Associates, Inc., 2023, pp. 11515--11551.
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Muschalik, Maximilian, et al. “Agnostic Explanation of Model Change Based on Feature Importance.”10.1007/s13218-022-00766-6__KI - Künstliche Intelligenz, vol. 36, no. 3–4, Springer Science and Business Media LLC, 2022, pp. 211–24, doi:10.1007/s13218-022-00766-6.
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