Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models

Author:

Il Idrissi Marouane1,Bousquet Nicolas1,Gamboa Fabrice2,Iooss Bertrand1,Loubes Jean-Michel2

Affiliation:

1. PRISME Department, EDF R&D Lab Chatou

2. Institut de Mathématiques de Toulouse, Université de Toulouse III - Paul Sabatier

Publisher

Institute of Mathematical Statistics

Reference89 articles.

1. A. Alfonsi and B. Jourdain. A remark on the optimal transport between two probability measures sharing the same copula. Statistics & Probability Letters, 84:131–134, January 2014.

2. D.L. Allaire and K. E. Willcox. Distributional sensitivity analysis. Procedia – Social and Behavioral Sciences, 2:7595–7596, 2010.

3. M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70, pages 214–223, 2017.

4. A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok. Synthesizing robust adversarial examples. In Jennifer G. Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning (ICML), 10-15, 2018, volume 80, pages 284–293, 2018.

5. European Banking Authority. 2021 EU-Wide Stress Test. European Banking Authority, 2020.

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