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Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space.
On the translocation of masses
Leonid Vitalievich Kantorovich · 1942
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A fast algorithm for the minimum covariance determinant estimator
Peter J Rousseeuw and Katrien Van Driessen · 1999
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Support vector method for novelty detection
Bernhard Schölkopf, Robert C Williamson, Alexander J Smola, John Shawe-Taylor, John C Platt, et al · 1999
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Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
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On minimum Kantorovich distance estimators
Federico Bassetti, Antonella Bodini, and Eugenio Regazzini · 2006
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Robust Statistics
Peter J. Huber and Elvezio Ronchetti · 2009
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Fast and robust earth mover’s distances
Ofir Pele and Michael Werman · 2009
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Optimal Transport: Old and New. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathemtical Sciences]
C. Villani · 2009
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Free boundaries in optimal transport and Monge-Ampere obstacle problems
Luis A Caffarelli and Robert J McCann · 2010
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The optimal partial transport problem
Alessio Figalli · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Sinkhorn Distances: Lightspeed Computation of Optimal Transport
Marco Cuturi · 2013
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Domain adaptation with regularized optimal transport
Nicolas Courty, Rémi Flamary, and Devis Tuia · 2014
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Generalized Wasserstein distance and its application to transport equations with source
Benedetto Piccoli and Francesco Rossi · 2014
Cited alongside, same era.
From Word Embeddings To Document Distances
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger · 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
Cited alongside, same era.
CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 2016
Cited alongside, same era.
Stochastic optimization for large-scale optimal transport
Aude Genevay, Marco Cuturi, Gabriel Peyré, and Francis Bach · 2016
Cited alongside, same era.
Supervised word mover’s distance
Gao Huang, Chuan Guo, Matt J Kusner, Yu Sun, Fei Sha, and Kilian Q Weinberger · 2016
Robust estimation via generative adversarial networks
Gao Chao, Yao Yuan, and Zhu Weizhi · 2018
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Scaling algorithms for unbalanced optimal transport problems
Lenaic Chizat, Gabriel Peyré, Bernhard Schmitzer, and François-Xavier Vialard · 2018
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Optimal entropy-transport problems and a new Hellinger–Kantorovich distance between positive measures
Matthias Liero, Alexander Mielke, and Giuseppe Savaré · 2018
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Computational Optimal Transport
Gabriel Peyré and Marco Cuturi · 2018
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Large-Scale Optimal Transport and Mapping Estimation
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, and Mathieu Blondel · 2018
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Cited alongside, same era.
f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Unbalanced optimal transport: Models, numerical methods, applications
Lenaïc Chizat · 2017
Cited alongside, same era.
Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy · 2017
Cited alongside, same era.
POT Python optimal transport library, 2017
Rémi Flamary and Nicolas Courty · 2017
Cited alongside, same era.
Multilevel clustering via Wasserstein means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, and Dinh Phung · 2017
Cited alongside, same era.
Sanvesh Srivastava, Cheng Li, and David B. Dunson · 2018
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Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 2019
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Single-model uncertainties for deep learning
Natasa Tagasovska and David Lopez-Paz · 2019
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Hierarchical Optimal Transport for Document Representation
Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, and Justin Solomon · 2019
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Robust optimal transport with applications in generative modeling and domain adaptation
Yogesh Balaji, Rama Chellappa, and Soheil Feizi · 2020
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When OT meets MOM: Robust estimation of Wasserstein distance
Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, and Florence d’Alché Buc · 2020
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Fixing bias in reconstruction-based anomaly detection with lipschitz discriminators
Alexander Tong, Guy Wolf, and Smita Krishnaswamyt · 2020
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On Minimax Optimality of GANs for Robust Mean Estimation
Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, and Yaoliang Yu · 2020
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