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Scaling Guarantees for Nearest Counterfactual Explanations.

, , , и . AIES, стр. 177-187. ACM, (2021)

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Amir Hossein Nahali

Amir Hossein Hakimhashemi

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Data-Driven Approximation of the Perron-Frobenius Operator Using the Wasserstein Metric., и . CoRR, (2020)Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness.. ETH Zurich, Zürich, Switzerland, (2023)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/642546).Scaling Guarantees for Nearest Counterfactual Explanations., , , и . AIES, стр. 177-187. ACM, (2021)On the Relationship Between Explanation and Prediction: A Causal View., , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 15861-15883. PMLR, (2023)Prospects and challenges of cancer systems medicine: from genes to disease networks., , , , и . Briefings Bioinform., (2022)Regression analysis of distributional data through Multi-Marginal Optimal transport., и . CoRR, (2021)On the Adversarial Robustness of Causal Algorithmic Recourse., , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 5324-5342. PMLR, (2022)On Data Manifolds Entailed by Structural Causal Models., , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 8188-8201. PMLR, (2023)Model-Agnostic Counterfactual Explanations for Consequential Decisions., , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 895-905. PMLR, (2020)Distance Correlation Autoencoder., , и . IJCNN, стр. 1-8. IEEE, (2018)