[3] L. Ambrosio and A. Pratelli. Existence and stability results in the L1 theory of optimal transportation, pages 123-160. Springer Berlin Heidelberg, Berlin, Heidelberg, 2003.
[4] M. Ancona, E. Ceolini, A. C. Öztireli, and M. H. Gross. A unified view of gradient-based attribution methods for deep neural networks. CoRR, 2017.
[5] C. Anil, J. Lucas, and R. Grosse. Sorting out Lipschitz function approximation. In K. Chaudhuri and R. Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 291-301, Long Beach, California, USA, June 2019. PMLR.
[6] A. Araujo, A. J. Havens, B. Delattre, A. Allauzen, and B. Hu. A unified algebraic perspective on lipschitz neural networks. In The Eleventh International Conference on Learning Representations, 2023.
[7] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks. In D. Precup and Y. W. Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 214-223, International Convention Centre, Sydney, Australia, Aug. 2017. PMLR.
[8] L. Béthane, A. González-Sanz, F. Mamalet, and M. Serrurier. The many faces of 1-lipschitz neural networks. CoRR, abs/2104.05097, 2021.
[9] U. Bhatt, A. Weller, and J. M. F. Moura. Evaluating and aggregating feature-based model explanations. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, 2020.
[10] Å. Björck and C. Bowie. An Iterative Algorithm for Computing the Best Estimate of an Orthogonal Matrix. SIAM Journal on Numerical Analysis, 8(2):358-364, June 1971.
[11] E. Black, S. Yeom, and M. Fredrikson. Fliptest: Fairness auditing via optimal transport. CoRR, abs/1906.09218, 2019.
[12] A. Boopathy, S. Liu, G. Zhang, C. Liu, P.-Y. Chen, S. Chang, and L. Daniel. Proper network interpretability helps adversarial robustness in classification. In International Conference on Machine Learning, 2020.
[13] N. Carlini and D. Wagner. Towards evaluating the robustness of neural networks. In 2017 IEEE symposium on security and privacy (sp), pages 39-57. IEEE, 2017.
[14] P. Chalasani, J. Chen, A. R. Chowdhury, S. Jha, and X. Wu. Concise explanations of neural networks using adversarial training. In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
[15] L. de Lara, A. González-Sanz, N. Asher, and J.-M. Loubes. Transport-based counterfactual models. arxiv:2108.13025, 2021.
[16] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09, 2009.
[17] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009.
[18] A.-K. Dombrowski, C. J. Anders, K.-R. Müller, and P. Kessel. Towards robust explanations for deep neural networks. volume 121, page 108194, 2022.
[19] C. Etmann, S. Lunz, P. Maass, and C.-B. Schonlieb. On the connection between adversarial robustness and saliency map interpretability. In International Conference on Machine Learning, 2019.
[20] T. Fel, R. Cadène, M. Chalvidal, M. Cord, D. Vigouroux, and T. Serre. Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis. CoRR, abs/2111.04138, 2021.