[1] J. Altschuler, J. Niles-Weed, and P. Rigollet. Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. In Advances in Neural Information Processing Systems, pages 1964–1974, 2017.
[2] Y. Bai, B. Schmitzer, M. Thorpe, and S. Kolouri. Sliced optimal partial transport. arXiv preprint arXiv:2212.08049, 2022.
[3] V. I. Bogachev and M. A. S. Ruas. Measure theory, volume 1. Springer, 2007.
[4] C. Bonet, P. Berg, N. Courty, F. Septier, L. Drumetz, and M.-T. Pham. Spherical sliced-wasserstein. arXiv preprint arXiv:2206.08780, 2022.
[5] C. Bonet, N. Courty, F. Septier, and L. Drumetz. Efficient gradient flows in sliced-Wasserstein space. Transactions on Machine Learning Research, 2022.
[6] N. Bonneel and D. Coeurjolly. Spot: sliced partial optimal transport. ACM Transactions on Graphics (TOG), 38(4):1-13, 2019.
[7] N. Bonneel, J. Rabin, G. Peyré, and H. Pfister. Sliced and Radon Wasserstein barycenters of measures. Journal of Mathematical Imaging and Vision, 1(51):22-45, 2015.
[8] N. Bonnotte. Unidimensional and evolution methods for optimal transportation. PhD thesis, Paris 11, 2013.
[9] S. Bubeck. Convex optimization: Algorithms and complexity. Foundations and Trends® in Machine Learning, 8(3-4):231-357, 2015.
[10] X. Chen, Y. Yang, and Y. Li. Augmented sliced Wasserstein distances. International Conference on Learning Representations, 2022.
[11] M. Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems, pages 2292-2300, 2013.
[12] B. Dai and U. Seljak. Sliced iterative normalizing flows. In International Conference on Machine Learning, pages 2352-2364. PMLR, 2021.
[13] T. R. Davidson, L. Falorsi, N. De Cao, T. Kipf, and J. M. Tomczak. Hyperspherical variational auto-encoders. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 856–865. Association For Uncertainty in Artificial Intelligence (AUAI), 2018.
[14] I. Deshpande, Y.-T. Hu, R. Sun, A. Pyrros, N. Siddiqui, S. Koyejo, Z. Zhao, D. Forsyth, and A. G. Schwing. Max-sliced Wasserstein distance and its use for GANs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10648-10656, 2019.
[15] I. Deshpande, Z. Zhang, and A. G. Schwing. Generative modeling using the sliced Wasserstein distance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3483-3491, 2018.
[16] L. Devroye, L. Györfi, and G. Lugosi. A probabilistic theory of pattern recognition, volume 31. Springer Science & Business Media, 2013.
[17] J. Feydy, T. Ségurné, F.-X. Vialard, S.-i. Amari, A. Trouve, and G. Peyre. Interpolating between optimal transport and MMD using Sinkhorn divergences. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 2681-2690, 2019.
[18] R. Flamary, N. Courty, A. Gramfort, M. Z. Alaya, A. Boisbunon, S. Chambon, L. Chapel, A. Corenflos, K. Fatras, N. Fournier, L. Gautheron, N. T. Gayraud, H. Janati, A. Rakotomamonjy, I. Redko, A. Rolet, A. Schutz, V. Seguy, D. J. Sutherland, R. Tavenard, A. Tong, and T. Vayer. Pot: Python optimal transport. Journal of Machine Learning Research, 22(78):1-8, 2021.
[19] N. Fournier and A. Guillin. On the rate of convergence in Wasserstein distance of the empirical measure. Probability Theory and Related Fields, 162:707-738, 2015.