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Recently, the Gromov-Wasserstein Optimal Transport (GWOT) problem has attracted the special attention of the ML community.
Foundations of modern probability , volume 2
Olav Kallenberg · 1997
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On the use of Gromov-Hausdorff Distances for Shape Comparison
Facundo Memoli · 2007
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Optimal transport: old and new , volume 338
Cédric Villani · 2008
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Spectral gromov-wasserstein distances for shape matching
Facundo Mémoli · 2009
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Gromov–wasserstein distances and the metric approach to object matching
Facundo Mémoli · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Optimal transportation for example-guided color transfer
Oriel Frigo, Neus Sabater, Vincent Demoulin, and Pierre Hellier · 2015
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Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs, and Modeling
F. Santambrogio · 2015
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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2016
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Optimal spectral transportation with application to music transcription
Rémi Flamary, Cédric Févotte, Nicolas Courty, and Valentin Emiya · 2016
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Fasttext.zip: Compressing text classification models
Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hérve Jégou, and Tomas Mikolov · 2016
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Gromov-wasserstein averaging of kernel and distance matrices
Gabriel Peyré, Marco Cuturi, and Justin Solomon · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou · 2017
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Kantorovich duality for general transport costs and applications
Nathael Gozlan, Cyril Roberto, Paul-Marie Samson, and Prasad Tetali · 2017
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Gromov-wasserstein alignment of word embedding spaces
David Alvarez-Melis and Tommi Jaakkola · 2018
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Interpolating between optimal transport and mmd using sinkhorn divergences, 2018
Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun ichi Amari, Alain Trouvé, and Gabriel Peyré · 2018
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BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
Benjamin Heinzerling and Michael Strube · 2018
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WGAN domain adaptation for eeg-based emotion recognition
Yun Luo, Si-Yang Zhang, Wei-Long Zheng, and Bao-Liang Lu · 2018
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Which training methods for gans do actually converge?, 2018
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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Improving GANs using optimal transport
Tim Salimans, Han Zhang, Alec Radford, and Dimitris Metaxas · 2018
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Existence, duality, and cyclical monotonicity for weak transport costs
Julio Backhoff-Veraguas, Mathias Beiglböck, and Gudmun Pammer · 2019
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The gromov–wasserstein distance between networks and stable network invariants
Samir Chowdhury and Facundo Mémoli · 2019
Cited alongside, same era.
Unsupervised alignment of embeddings with wasserstein procrustes
Edouard Grave, Armand Joulin, and Quentin Berthet · 2019
Cited alongside, same era.
Trivializations for gradient-based optimization on manifolds
Mario Lezcano-Casado · 2019
Cited alongside, same era.
Computational optimal transport
Gabriel Peyré, Marco Cuturi, et al · 2019
Cited alongside, same era.
Optimal transport for multi-source domain adaptation under target shift
Ievgen Redko, Nicolas Courty, Rémi Flamary, and Devis Tuia · 2019
Cited alongside, same era.
Scalable gromov-wasserstein learning for graph partitioning and matching
Hongteng Xu, Dixin Luo, and Lawrence Carin · 2019
Cited alongside, same era.
Generative modeling with optimal transport maps
Litu Rout, Alexander Korotin, and Evgeny Burnaev · 2022
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Linear-time gromov wasserstein distances using low rank couplings and costs
Meyer Scetbon, Gabriel Peyré, and Marco Cuturi · 2022
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Semi-relaxed gromov-wasserstein divergence and applications on graphs
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, and Nicolas Courty · 2022
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Generative modeling through the semi-dual formulation of unbalanced optimal transport
Jaemoo Choi, Jaewoong Choi, and Myungjoo Kang · 2023
Closest in time.
Neural monge map estimation and its applications
Jiaojiao Fan, Shu Liu, Shaojun Ma, Hao-Min Zhou, and Yongxin Chen · 2023
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Generative entropic neural optimal transport to map within and across spaces
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Graph optimal transport for cross-domain alignment
Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, and Jingjing Liu · 2020
Cited alongside, same era.
Optimal transport mapping via input convex neural networks
Ashok Makkuva, Amirhossein Taghvaei, Sewoong Oh, and Jason Lee · 2020
Cited alongside, same era.
The unbalanced gromov wasserstein distance: Conic formulation and relaxation
Thibault Séjourné, François-Xavier Vialard, and Gabriel Peyré · 2020
Cited alongside, same era.
A contribution to optimal transport on incomparable spaces
Titouan Vayer · 2020
Cited alongside, same era.
Generalized spectral clustering via gromov-wasserstein learning
Samir Chowdhury and Tom Needham · 2021
Cited alongside, same era.
Score-based generative neural networks for large-scale optimal transport
Grady Daniels, Tyler Maunu, and Paul Hand · 2021
Cited alongside, same era.
Dominik Klein, Théo Uscidda, Fabian Theis, and Marco Cuturi · 2023
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Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
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Recent advances in optimal transport for machine learning
Eduardo Fernandes Montesuma, Fred Ngole Mboula, and Antoine Souloumiac · 2023
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The monge gap: A regularizer to learn all transport maps
Théo Uscidda and Marco Cuturi · 2023
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Neural entropic gromov-wasserstein alignment
Tao Wang and Ziv Goldfeld · 2023
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Neural optimal transport with general cost functionals
Arip Asadulaev, Alexander Korotin, Vage Egiazarian, Petr Mokrov, and Evgeny Burnaev · 2024
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Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, and Titouan Vayer · 2024
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Breaking isometric ties and introducing priors in Gromov-Wasserstein distances
Pinar Demetci, Quang Huy Tran, Ievgen Redko, and Ritambhara Singh · 2024
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On the existence of monge maps for the gromov–wasserstein problem
Théo Dumont, Théo Lacombe, and François-Xavier Vialard · 2024
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Entropic neural optimal transport via diffusion processes
Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry P Vetrov, and Evgeny Burnaev · 2024
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Comparison results for gromov–wasserstein and gromov–monge distances
Facundo Mémoli and Tom Needham · 2024
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Energy-guided entropic neural optimal transport
Petr Mokrov, Alexander Korotin, Alexander Kolesov, Nikita Gushchin, and Evgeny Burnaev · 2024
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Entropic estimation of optimal transport maps, 2024
Aram-Alexandre Pooladian and Jonathan Niles-Weed · 2024
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Cross-modality matching and prediction of perturbation responses with labeled gromov-wasserstein optimal transport, 2024
Jayoung Ryu, Romain Lopez, Charlotte Bunne, and Aviv Regev · 2024
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Structured transforms across spaces with cost-regularized optimal transport
Othmane Sebbouh, Marco Cuturi, and Gabriel Peyré · 2024
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Strongly isomorphic neural optimal transport across incomparable spaces
Athina Sotiropoulou and David Alvarez-Melis · 2024
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Disentangled representation learning through geometry preservation with the gromov-monge gap
Théo Uscidda, Luca Eyring, Karsten Roth, Fabian J Theis, Zeynep Akata, and marco cuturi · 2024
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Gromov–wasserstein distances: Entropic regularization, duality and sample complexity
Zhengxin Zhang, Ziv Goldfeld, Youssef Mroueh, and Bharath K Sriperumbudur · 2024
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Quadratic-form optimal transport, 2025
Ruodu Wang and Zhenyuan Zhang · 2025
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