Marco Cuturi

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Gabriel PeyréCNRS, DMA, Ecole Normale SupérieureVerified email at ens.fr
Jean-Philippe VertOwkin / PSL UniversityVerified email at mines-paristech.fr
Aude GenevayApplied Scientist, AmazonVerified email at amazon.com
Justin SolomonMITVerified email at mit.edu
Alexandre GramfortMetaVerified email at meta.com
Olivier TeboulTherapanaceaVerified email at therapanacea.eu
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Hicham JanatiAssociate professor, Télécom ParisVerified email at telecom-paris.fr
Arnaud DoucetProfessor of Statistics, University of Oxford & Senior Research Scientist, Google DeepMindVerified email at stats.ox.ac.uk
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Tianyi LinUniversity of California, BerkeleyVerified email at berkeley.edu
Mathieu BlondelGoogleVerified email at google.com
Nicolas BonneelCNRS / LIRIS / Univ. LyonVerified email at liris.cnrs.fr
Nhat HoProfessor at University of Texas, AustinVerified email at utexas.edu
Fernando de GoesPixar Animation StudiosVerified email at pixar.com
Leonidas GuibasProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Adrian ButscherAutodeskVerified email at autodesk.com
Tao DuInstitute for Interdisciplinary Information Sciences (IIIS), Tsinghua UniversityVerified email at tsinghua.edu.cn
Boris MuzellecOwkinVerified email at owkin.com
Meyer ScetbonResearcher, MicrosoftVerified email at microsoft.com

Marco Cuturi

Apple / CREST-ENSAE.

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machine learning optimal transport


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Sinkhorn Distances: Lightspeed Computation of Optimal Transport M Cuturi Proceedings of the 26th International Conference on Advances in Neural …, 2013	3166	2013
Computational Optimal Transport G Peyré, M Cuturi Foundations and Trends in Machine Learning 11 (5-6), pp. 355-607, 2019	2265	2019
Iterative Bregman projections for regularized transportation problems JD Benamou, G Carlier, M Cuturi, L Nenna, G Peyré SIAM Journal on Scientific Computing 37 (2), A1111-A1138, 2015	748	2015
Fast computation of Wasserstein barycenters M Cuturi, A Doucet Proceedings of the International Conference on Machine Learning 2014, JMLR …, 2014	706	2014
Convolutional Wasserstein Distances: Efficient Optimal Transportation on Geometric Domains J Solomon, F de Goes, G Peyré, M Cuturi, A Butscher, A Nguyen, T Du, ... ACM Transactions on Graphics (TOG) SIGGRAPH, 2015, 2015	628	2015
Learning Generative Models with Sinkhorn Divergences A Genevay, G Peyré, M Cuturi Proceedings of the Twenty-First International Conference on Artifical …, 2017	615*	2017
Soft-DTW: a differentiable loss function for time-series M Cuturi, M Blondel Proceedings of the 34th International Conference on Machine Learning, PMLR …, 2017	513	2017
Stochastic optimization for large-scale optimal transport A Genevay, M Cuturi, G Peyré, F Bach arXiv preprint arXiv:1605.08527, 2016	435	2016
Fast global alignment kernels M Cuturi International Conference in Machine Learning 2011, 2011	409	2011
On wasserstein two-sample testing and related families of nonparametric tests A Ramdas, N García Trillos, M Cuturi Entropy 19 (2), 47, 2017	371	2017
A kernel for time series based on global alignments M Cuturi, JP Vert, O Birkenes, T Matsui Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE …, 2007	296	2007
Gromov-wasserstein averaging of kernel and distance matrices G Peyré, M Cuturi, J Solomon International conference on machine learning, 2664-2672, 2016	269	2016
Sliced Wasserstein kernel for persistence diagrams M Carriere, M Cuturi, S Oudot International conference on machine learning, 664-673, 2017	242	2017
Sample complexity of sinkhorn divergences A Genevay, L Chizat, F Bach, M Cuturi, G Peyré The 22nd international conference on artificial intelligence and statistics …, 2019	224	2019
A smoothed dual approach for variational wasserstein problems M Cuturi, G Peyré SIAM J. Imaging Sciences 9 (1), 320–343, 2016	191	2016
Wasserstein Barycentric Coordinates: Histogram Regression Using Optimal Transport N Bonneel, G Peyré, M Cuturi ACM Transactions on Graphics 35 (4), 2016	162	2016
Fast dictionary learning with a smoothed Wasserstein loss A Rolet, M Cuturi, G Peyré Artificial Intelligence and Statistics, 630-638, 2016	147	2016
Wasserstein dictionary learning: Optimal transport-based unsupervised nonlinear dictionary learning MA Schmitz, M Heitz, N Bonneel, F Ngole, D Coeurjolly, M Cuturi, G Peyré, ... SIAM Journal on Imaging Sciences 11 (1), 643-678, 2018	142	2018
Learning with differentiable perturbed optimizers Q Berthet, M Blondel, O Teboul, M Cuturi, JP Vert, F Bach NeurIPS 2020, 2020	132	2020
Wasserstein training of restricted Boltzmann machines G Montavon, KR Müller, M Cuturi Advances in Neural Information Processing Systems 29, 2016	129*	2016

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