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Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions.
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Optimal Transport: Old and New
Cédric Villani · 2008
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Manifold alignment without correspondence
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Adapting visual category models to new domains
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Residual bayesian co-clustering for matrix approximation
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Gromov wasserstein distances and the metric approach to object matching
Learning discriminative correlation subspace for heterogeneous domain adaptation
Yuguang Yan, Wen Li, Michael Ng, Mingkui Tan, Hanrui Wu, Huaqing Min, and Qingyao Wu · 2017
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Co-clustering through optimal transport
Charlotte Laclau, Ievgen Redko, Basarab Matei, Younès Bennani, and Vincent Brault · 2017
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Gromov-Wasserstein Alignment of Word Embedding Spaces
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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Semi-supervised optimal transport for heterogeneous domain adaptation
Yuguang Yan, Wen Li, Hanrui Wu, Huaqing Min, Mingkui Tan, and Qingyao Wu · 2018
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(probably) concave graph matching
Haggai Maron and Yaron Lipman · 2018
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Towards optimal transport with global invariances
David Alvarez-Melis, Stefanie Jegelka, and Tommi S. Jaakkola · 2019
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Facundo Memoli · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Generalized unsupervised manifold alignment
Zhen Cui, Hong Chang, Shiguang Shan, and Xilin Chen · 2014
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Heterogeneous domain adaptation and classification by exploiting the correlation subspace
Yi-Ren Yeh, Chun-Hao Huang, and Yu-Chiang Frank Wang · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Unsupervised alignment of embeddings with wasserstein procrustes
Edouard Grave, Armand Joulin, and Quentin Berthet · 2019
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Sample complexity of sinkhorn divergences
Aude Genevay, Lénaïc Chizat, Francis Bach, Marco Cuturi, and Gabriel Peyré · 2019
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How similar are two elections?
P. Faliszewski, P. Skowron, A. Slinko, S. Szufa, and N. Talmon · 2019
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Learning generative models across incomparable spaces
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, and Stefanie Jegelka · 2019
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Massively scalable sinkhorn distances via the nyström method
Jason Altschuler, Francis Bach, Alessandro Rudi, and Jonathan Niles-Weed · 2019
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Screening Sinkhorn Algorithm for Regularized Optimal Transport
Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, and Alain Rakotomamonjy · 2019
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Optimal transport for structured data with application on graphs
Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, and Nicolas Courty · 2019
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Hierarchical optimal transport for multimodal distribution alignment
John Lee, Max Dabagia, Eva Dyer, and Christopher Rozell · 2019
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Hierarchical optimal transport for document representation
Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, and Justin M. Solomon · 2019
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Optimal transport for multi-source domain adaptation under target shift
I. Redko, N. Courty, R. Flamary, and D. Tuia · 2019
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Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
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Teaser: Fast and certifiable point cloud registration, 2020
Heng Yang, Jingnan Shi, and Luca Carlone · 2020
Closest in time.
Geometric dataset distances via optimal transport, 2020
David Alvarez-Melis and Nicolò Fusi · 2020
Closest in time.