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Optimal Transport (OT) is being widely used in various fields such as machine learning and computer vision, as it is a powerful tool for measuring the similarity between probability distributions and histograms.
Numerical resolution of an “unbalanced” mass transport problem
Jean-David Benamou · 2003
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Inference with aggregate data: An optimal transport approach
Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, and Yongxin Chen · 2003
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Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later
Cleve Moler, Charles Van Loan · 2003
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The Sinkhorn–Knopp algorithm: convergence and applications
Philip A Knight · 2008
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Barycenters in the Wasserstein space
Martial Agueh and Guillaume Carlier · 2011
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Collective graphical models
Daniel R. Sheldon and Thomas G. Dietterich · 2011
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Markov chains: Gibbs fields, Monte Carlo simulation, and queues
Pierre Brémaud · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Approximate inference in collective graphical models
Daniel Sheldon, Tao Sun, Akshat Kumar, and Tom Dietterich · 2013
Cited alongside, same era.
Population estimation technology for mobile spatial statistics
Masayuki Terada, Tomohiro Nagata, and Motonari Kobayashi · 2013
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Ground metric learning
Marco Cuturi and David Avis · 2014
Cited alongside, same era.
Wasserstein propagation for semi-supervised learning
Justin Solomon, Raif Rustamov, Leonidas Guibas, and Adrian Butscher · 2014
Cited alongside, same era.
Learning with a Wasserstein loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, and Tomaso A Poggio · 2015
Cited alongside, same era.
Convolutional Wasserstein distances: Efficient optimal transportation on geometric domains
Justin Solomon, Fernando De Goes, Gabriel Peyré, Marco Cuturi, Adrian Butscher, Andy Nguyen, Tao Du, and Leonidas Guibas · 2015
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2017
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Pot python optimal transport library, 2017
R’emi Flamary and Nicolas Courty · 2017
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Gini-regularized optimal transport with an application to spatio-temporal forecasting
Lucas Roberts, Leo Razoumov, Lin Su, and Yuyang Wang · 2017
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Smooth and sparse optimal transport
Mathieu Blondel, Vivien Seguy, and Antoine Rolet · 2018
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Scaling algorithms for unbalanced optimal transport problems
Lenaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, and François-Xavier Vialard · 2018
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Cited alongside, same era.
Message passing for collective graphical models
Tao Sun, Daniel Sheldon, and Akshat Kumar · 2015
Cited alongside, same era.
Approximate inference using DC programming for collective graphical models
Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, and Daniel Sheldon · 2016
Cited alongside, same era.
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Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
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