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Optimal transport (OT) is a powerful framework to compare probability measures, a fundamental task in many statistical and machine learning problems.
The speed of mean glivenko-cantelli convergence
Richard Mansfield Dudley · 1969
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Thumbs up? sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan · 2002
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
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Gradient flows: in metric spaces and in the space of probability measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2005
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Minimax and monotonicity
Stephen Simons · 2006
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Measure theory , volume 1
Vladimir Igorevich Bogachev and Maria Aparecida Soares Ruas · 2007
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Fast and robust earth mover’s distances
Ofir Pele and Michael Werman · 2009
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Regularization of transportation maps for color and contrast transfer
Julien Rabin, Julie Delon, and Yann Gousseau · 2010
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Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2012
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Unidimensional and evolution methods for optimal transportation
Nicolas Bonnotte · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Regularized discrete optimal transport
Sira Ferradans, Nicolas Papadakis, Julien Rabin, Gabriel Peyré, and Jean-François Aujol · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Generalized wasserstein distance and its application to transport equations with source
Benedetto Piccoli and Francesco Rossi · 2014
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Sliced and radon wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
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From word embeddings to document distances
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger · 2015
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On the global linear convergence of frank-wolfe optimization variants
Simon Lacoste-Julien and Martin Jaggi · 2015
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A representative democracy to reduce interdependency in a multimodel ensemble
Benjamin M Sanderson, Reto Knutti, and Peter Caldwell · 2015
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Optimal transport for applied mathematicians
Filippo Santambrogio · 2015
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Wasserstein barycentric coordinates: histogram regression using optimal transport
Nicolas Bonneel, Gabriel Peyré, and Marco Cuturi · 2016
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Variance-reduced and projection-free stochastic optimization
Elad Hazan and Haipeng Luo · 2016
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A fitness-driven cross-diffusion system from population dynamics as a gradient flow
Stanislav Kondratyev, Léonard Monsaingeon, and Dmitry Vorotnikov · 2016
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A multi-task approach to predict likability of books
Suraj Maharjan, John Arevalo, Manuel Montes, Fabio A González, and Thamar Solorio · 2017
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Multimodal machine learning: A survey and taxonomy
Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency · 2018
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On the global convergence of gradient descent for over-parameterized models using optimal transport
Lenaic Chizat and Francis Bach · 2018
Cited alongside, same era.
Optimal entropy-transport problems and a new hellinger–kantorovich distance between positive measures
On unbalanced optimal transport: An analysis of Sinkhorn algorithm
Khiem Pham, Khang Le, Nhat Ho, Tung Pham, and Hung Bui · 2020
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Fast unbalanced optimal transport on a tree
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2020
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Strong equivalence between metrics of Wasserstein type
Erhan Bayraktar and Gaoyue Guo · 2021
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Unbalanced optimal transport through non-negative penalized linear regression
Laetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte, and Gilles Gasso · 2021
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Unbalanced minibatch optimal transport; applications to domain adaptation
Kilian Fatras, Thibault Sejourne, Rémi Flamary, and Nicolas Courty · 2021
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, et al · 2021
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Matthias Liero, Alexander Mielke, and Giuseppe Savaré · 2018
Cited alongside, same era.
Spot: sliced partial optimal transport
Nicolas Bonneel and David Coeurjolly · 2019
Cited alongside, same era.
Max-sliced wasserstein distance and its use for gans
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, and Alexander G Schwing · 2019
Cited alongside, same era.
Sample complexity of sinkhorn divergences
Aude Genevay, Lénaic Chizat, Francis Bach, Marco Cuturi, and Gabriel Peyré · 2019
Cited alongside, same era.
Generalized sliced wasserstein distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, and Gustavo Rohde · 2019
Cited alongside, same era.
A wrapped normal distribution on hyperbolic space for gradient-based learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, and Masanori Koyama · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Sliced mutual information: A scalable measure of statistical dependence
Ziv Goldfeld and Kristjan Greenewald · 2021
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On robust optimal transport: Computational complexity and barycenter computation
Khang Le, Huy Nguyen, Quang M Nguyen, Tung Pham, Hung Bui, and Nhat Ho · 2021
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Entropy partial transport with tree metrics: Theory and practice
Tam Le and Truyen Nguyen · 2021
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Climatenet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
Prabhat, K. Kashinath, M. Mudigonda, S. Kim, L. Kapp-Schwoerer, A. Graubner, E. Karaismailoglu, L. von Kleist, T. Kurth, A. Greiner, A. Mahesh, K. Yang, C. Lewis, J. Chen, A. Lou, S. Chandran, B. Toms, W. Chapman, K. Dagon, C. A. Shields, T. O’Brien, M. Wehner, and W. Collins · 2021
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Efficient gradient flows in sliced-wasserstein space
Clément Bonet, Nicolas Courty, François Septier, and Lucas Drumetz · 2022
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Scotv2: Single-cell multiomic alignment with disproportionate cell-type representation
Pinar Demetci, Rebecca Santorella, Manav Chakravarthy, Bjorn Sandstede, and Ritambhara Singh · 2022
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Combining global climate models using graph cuts
Soulivanh Thao, Mats Garvik, Gregoire Mariethoz, and Mathieu Vrac · 2022
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Distributional convergence of the sliced wasserstein process
Jiaqi Xi and Jonathan Niles-Weed · 2022
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URL "https://docs.midjourney.com/legacy/en"
Midjourney, 2023 · 2023
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Sliced optimal partial transport
Yikun Bai, Bernhard Schmitzer, Matthew Thorpe, and Soheil Kolouri · 2023
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Hierarchical sliced wasserstein distance
Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Minh Nguyen, and Nhat Ho · 2023
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Shedding a pac-bayesian light on adaptive sliced-wasserstein distances
Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, and Liva Ralaivola · 2023
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Semi-dual unbalanced quadratic optimal transport: fast statistical rates and convergent algorithm
Adrien Vacher and François-Xavier Vialard · 2023
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