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Convolutional wasserstein distances: efficient optimal transportation on geometric domains.

, , , , , , , and . ACM Trans. Graph., 34 (4): 66:1-66:11 (2015)

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Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space., , , , and . SIAM J. Sci. Comput., (2018)Sample Complexity of Sinkhorn Divergences, , , , and . Proceedings of Machine Learning Research, volume 89 of Proceedings of Machine Learning Research, page 1574--1583. PMLR, (16--18 Apr 2019)Low-rank Optimal Transport: Approximation, Statistics and Debiasing., and . NeurIPS, (2022)Efficient and Modular Implicit Differentiation., , , , , , , and . NeurIPS, (2022)Tree-Sliced Variants of Wasserstein Distances., , , and . NeurIPS, page 12283-12294. (2019)Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 6671-6682. PMLR, (2023)Rethinking Initialization of the Sinkhorn Algorithm., and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 8682-8698. PMLR, (2023)Low-Rank Sinkhorn Factorization., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 9344-9354. PMLR, (2021)Stochastic Deep Networks, , and . (2018)cite arxiv:1811.07429.Wasserstein regularization for sparse multi-task regression., , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1407-1416. PMLR, (2019)