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Learning Generative Models with Sinkhorn Divergences., , and . AISTATS, volume 84 of Proceedings of Machine Learning Research, page 1608-1617. PMLR, (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)Sample Complexity of Sinkhorn Divergences., , , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1574-1583. PMLR, (2019)Improving approximate optimal transport distances using quantization., , , and . UAI, volume 161 of Proceedings of Machine Learning Research, page 290-300. AUAI Press, (2021)Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark., , , , , and . NeurIPS, page 14593-14605. (2021)Continuous Regularized Wasserstein Barycenters., , , and . NeurIPS, (2020)Stochastic Optimization for Large-scale Optimal Transport., , , and . NIPS, page 3432-3440. (2016)Transfer Learning for User Adaptation in Spoken Dialogue Systems., and . AAMAS, page 975-983. ACM, (2016)Entropy-Regularized Optimal Transport for Machine Learning. (Régularisation Entropique du Transport Optimal pour le Machine Learning).. PSL Research University, Paris, France, (2019)Large-Scale Wasserstein Gradient Flows., , , , , and . NeurIPS, page 15243-15256. (2021)