[20] A. Geneva, G. Peyre, and M. Cuturi. Learning generative models with Sinkhorn divergences. In International Conference on Artificial Intelligence and Statistics, pages 1608-1617. PMLR, 2018.
[21] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In Advances in Neural Information Processing Systems, pages 6626-6637, 2017.
[22] M. Huang, S. Ma, and L. Lai. A Riemannian block coordinate descent method for computing the projection robust Wasserstein distance. In International Conference on Machine Learning, pages 4446-4455. PMLR, 2021.
[23] P. E. Jupp and K. V. Mardia. Maximum likelihood estimators for the matrix von Mises-Fisher and Bingham distributions. The Annals of Statistics, 7(3):599-606, 1979.
[24] O. Kallenberg and O. Kallenberg. Foundations of modern probability, volume 2. Springer, 1997.
[25] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[26] S. Kolouri, K. Nadjahi, U. Simsekli, R. Badeau, and G. Rohde. Generalized sliced Wasserstein distances. In Advances in Neural Information Processing Systems, pages 261-272, 2019.
[27] S. Kolouri, P. E. Pope, C. E. Martin, and G. K. Rohde. Sliced Wasserstein auto-encoders. In International Conference on Learning Representations, 2018.
[28] S. Kolouri, G. K. Rohde, and H. Hoffmann. Sliced Wasserstein distance for learning Gaussian mixture models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3427-3436, 2018.
[29] A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. Master's thesis, Department of Computer Science, University of Toronto, 2009.
[30] C.-Y. Lee, T. Batra, M. H. Baig, and D. Ulbricht. Sliced Wasserstein discrepancy for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10285-10295, 2019.
[31] J. Lezama, W. Chen, and Q. Qiu. Run-sort-erun: Escaping batch size limitations in sliced Wasserstein generative models. In International Conference on Machine Learning, pages 6275–6285. PMLR, 2021.
[32] T. Lin, C. Fan, N. Ho, M. Cuturi, and M. Jordan. Projection robust Wasserstein distance and Riemannian optimization. Advances in Neural Information Processing Systems, 33:9383-9397, 2020.
[33] T. Lin, N. Ho, X. Chen, M. Cuturi, and M. I. Jordan. Fixed-support Wasserstein barycenters: Computational hardness and fast algorithm. In NeurIPS, pages 5368-5380, 2020.
[34] T. Lin, N. Ho, and M. Jordan. On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms. In International Conference on Machine Learning, pages 3982-3991, 2019.
[35] T. Lin, N. Ho, and M. I. Jordan. On the efficiency of entropic regularized algorithms for optimal transport. Journal of Machine Learning Research (JMLR), 23:1-42, 2022.
[36] Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015.
[37] A. Liutkus, U. Simsekli, S. Majewski, A. Durmus, and F.-R. Stöter. Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions. In International Conference on Machine Learning, pages 4104-4113. PMLR, 2019.
[38] N. Naderializadeh, J. Comer, R. Andrews, H. Hoffmann, and S. Kolouri. Pooling by sliced-Wasserstein embedding. Advances in Neural Information Processing Systems, 34, 2021.