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The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations.

, , , , and . IJCAI, page 2801-2807. ijcai.org, (2019)

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Localized Random Shapelets., , , and . AALTD@PKDD/ECML, volume 11986 of Lecture Notes in Computer Science, page 85-97. Springer, (2019)Imperceptible Adversarial Attacks on Tabular Data., , , , , and . CoRR, (2019)On the overlooked issue of defining explanation objectives for local-surrogate explainers., , , , and . CoRR, (2021)Defining Locality for Surrogates in Post-hoc Interpretablity., , , , and . CoRR, (2018)Dynamic Interpretability for Model Comparison via Decision Rules., , , and . CoRR, (2023)Understanding Prediction Discrepancies in Machine Learning Classifiers., , and . CoRR, (2021)Unjustified Classification Regions and Counterfactual Explanations in Machine Learning., , , , and . ECML/PKDD (2), volume 11907 of Lecture Notes in Computer Science, page 37-54. Springer, (2019)Random-shapelet: An algorithm for fast shapelet discovery., , , and . DSAA, page 1-10. IEEE, (2015)Inverse Classification for Comparison-based Interpretability in Machine Learning., , , , and . CoRR, (2017)Time series representation for classification: a motif-based approach. (Représentation de séries temporelles pour la classification: une approche basée sur la découverte automatique de motifs).. Pierre and Marie Curie University, Paris, France, (2017)