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Semi-relaxed Gromov-Wasserstein divergence and applications on graphs. C. Vincent-Cuaz, R. Flamary, M. Corneli, T. Vayer, N. Courty (2022). Template based graph neural network with optimal transport distances. H. Van Assel, C. Vincent-Cuaz, T. Vayer, R. Flamary, N. Courty (2023). Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein. Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications.<br><br><br> Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. Large-scale Optimal Transport and Mapping Estimation. Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal Transport. Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). Convolutional wasserstein distances: Efficient optimal transportation on geometric domains. Liutkus, A., Simsekli, U., Majewski, S., Durmus, A., & Stöter, F. R. (2019, May). Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. Delon, J., Gozlan, N., and Saint-Dizier, A. Generalized Wasserstein barycenters between probability measures living on different subspaces. Bonet, C., Berg, P., Courty, N., Septier, F., Drumetz, L., & Pham, M. T. (2023). Spherical Sliced-Wasserstein. Li, J., Tang, J., Kong, L., Liu, H., Li, J., So, A. M. C., & Blanchet, J. (2022). A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data.<br><br><br> Liu,  [https://www.dubaitowels.com/bed-sheets.html silk crib sheets] T., Puigcerver, J., & Blondel, M. (2023). Sparsity-constrained optimal transport. Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023). Entropic Wasserstein Component Analysis. NeurIPS 2023 Workshop Optimal Transport and Machine Learning. Statistical optimal transport via factored couplings. Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) Stochastic Optimization for Large-scale Optimal Transport. Fast transport optimization for Monge costs on the circle. The statistics of circular optimal transport. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Proceedings of the Eleventh International Conference on Learning Representations (ICLR). In The Eleventh International Conference on Learning Representations. In The Eleventh International Conference on Learning Representations (ICLR). In International Conference on Machine Learning (pp. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III 22 (pp. Forrow, A., Hütter, J. C., Nitzan, M., Rigollet, P., Schiebinger, G., & Weed, J. (2019, April). Feydy, J., Roussillon, P., Trouvé, A., & Gori, P. (2019). Fast and scalable optimal transport for brain tractograms. Charlier, B., Feydy, J., Glaunes, J. A., Collin, F. D., & Durif, G. (2021). Kernel operations on the gpu, with autodiff, without memory overflows.<br>