1. Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'e}on (2017) Wasserstein generative adversarial networks. PMLR, 214--223, International conference on machine learning
2. Dukler, Yonatan and Li, Wuchen and Lin, Alex and Mont{\'u}far, Guido (2019) Wasserstein of Wasserstein loss for learning generative models. PMLR, 1716--1725, International conference on machine learning
3. Xu, Hongteng and Wang, Wenlin and Liu, Wei and Carin, Lawrence (2018) Distilled wasserstein learning for word embedding and topic modeling. Advances in Neural Information Processing Systems 31
4. Chan, Zhangming and Li, Juntao and Yang, Xiaopeng and Chen, Xiuying and Hu, Wenpeng and Zhao, Dongyan and Yan, Rui (2019) Modeling personalization in continuous space for response generation via augmented wasserstein autoencoders. 1931--1940, Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (emnlp-ijcnlp)
5. Chizat, Lenaic and Roussillon, Pierre and L{\'e}ger, Flavien and Vialard, Fran{\c{c}}ois-Xavier and Peyr{\'e}, Gabriel (2020) Faster wasserstein distance estimation with the sinkhorn divergence. Advances in Neural Information Processing Systems 33: 2257--2269