Fetching the paper…
Reading the bibliography…
The notion of entropy-regularized optimal transport, also known as Sinkhorn divergence, has recently gained popularity in machine learning and statistics, as it makes feasible the use of smoothed optimal transportation distances for data analysis.
The use of entropy maximising models, in the theory of trip distribution, mode split and route split
A. G. Wilson · 1969
Earlier work this paper cites.
An Introduction to the Bootstrap
B. Efron and R. J. Tibshirani · 1993
Earlier work this paper cites.
Weak convergence and empirical processes
A. W. Van Der Vaart and J. A. Wellner · 1996
Earlier work this paper cites.
Tests of goodness of fit based on the L 2 L_{2} -Wasserstein distance
E. del Barrio, J. A. Cuesta-Albertos, C. Matrán, and J. M. Rodriguez-Rodriguez · 1999
Earlier work this paper cites.
Convex analysis in general vector spaces
C. Zalinescu · 2002
Earlier work this paper cites.
Topics in optimal transportation
C. Villani · 2003
Earlier work this paper cites.
Convex optimization
S. Boyd and L. Vandenberghe · 2004
Earlier work this paper cites.
A biologically inspired algorithm for the recovery of shading and reflectance images
A. Olmos and F. A. Kingdom · 2004
Earlier work this paper cites.
Asymptotics for L 2 L_{2} functionals of the empirical quantile process, with applications to tests of fit based on weighted Wasserstein distances
E. del Barrio, E. Giné, and F. Utzet · 2005
Earlier work this paper cites.
On Hadamard differentiability in k k -sample semiparametric models—with applications to the assessment of structural relationships
G. Freitag and A. Munk · 2005
Earlier work this paper cites.
All of statistics: a concise course in statistical inference
L. Wasserman · 2011
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
Earlier work this paper cites.
Convergence of latent mixing measures in finite and infinite mixture models
X. Nguyen · 2013
Earlier work this paper cites.
Fast computation of Wasserstein barycenters
M. Cuturi and A. Doucet · 2014
Earlier work this paper cites.
Learning with a Wasserstein loss
C. Frogner, C. Zhang, H. Mobahi, M. Araya, and T. A. Poggio · 2015
Cited alongside, same era.
Fast optimal transport averaging of neuroimaging data
A. Gramfort, G. Peyré, and M. Cuturi · 2015
Cited alongside, same era.
Convex color image segmentation with optimal transport distances
J. Rabin and N. Papadakis · 2015
Cited alongside, same era.
Principal geodesic analysis for probability measures under the optimal transport metric
V. Seguy and M. Cuturi · 2015
Cited alongside, same era.
Convolutional wasserstein distances: Efficient optimal transportation on geometric domains
J. Solomon, F. de Goes, G. Peyré, M. Cuturi, A. Butscher, A. Nguyen, T. Du, and L. Guibas · 2015
Cited alongside, same era.
A smoothed dual approach for variational Wasserstein problems
M. Cuturi and G. Peyré · 2016
Sinkhorn-autodiff: Tractable Wasserstein learning of generative models
A. Genevay, G. Peyré, and M. Cuturi · 2017
Closest in time.
On wasserstein two-sample testing and related families of nonparametric tests
A. Ramdas, N. G. Trillos, and M. Cuturi · 2017
Closest in time.
Wasserstein dictionary learning: Optimal transport-based unsupervised non-linear dictionary learning
M. A. Schmitz, M. Heitz, N. Bonneel, F. M. N. Mboula, D. Coeurjolly, M. Cuturi, G. Peyré, and J.-L. Starck · 2017
Closest in time.
Asymptotic analysis of objectives based on fisher information in active learning
J. Sourati, M. Akcakaya, T. K. Leen, D. Erdogmus, and J. G. Dy · 2017
Closest in time.
Overrelaxed sinkhorn-knopp algorithm for regularized optimal transport
A. Thibault, L. Chizat, C. Dossal, and N. Papadakis · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Stochastic optimization for large-scale optimal transport
A. Genevay, M. Cuturi, G. Peyré, and F. Bach · 2016
Cited alongside, same era.
Limit laws of the empirical Wasserstein distance: Gaussian distributions
T. Rippl, A. Munk, and A. Sturm · 2016
Cited alongside, same era.
Fast dictionary learning with a smoothed Wasserstein loss
A. Rolet, M. Cuturi, and G. Peyré · 2016
Cited alongside, same era.
Inference for empirical wasserstein distances on finite spaces
M. Sommerfeld and A. Munk · 2016
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
Geodesic pca in the Wasserstein space by convex pca
J. Bigot, R. Gouet, T. Klein, A. López, et al · 2017
Cited alongside, same era.
Fast discrete distribution clustering using Wasserstein barycenter with sparse support
J. Ye, P. Wu, J. Z. Wang, and J. Li · 2017
Closest in time.
Data-driven regularization of wasserstein barycenters with an application to multivariate density registration
J. Bigot, E. Cazelles, and N. Papadakis · 2018
Closest in time.
Geodesic pca versus log-pca of histograms in the wasserstein space
E. Cazelles, V. Seguy, J. Bigot, M. Cuturi, and N. Papadakis · 2018
Closest in time.
Inference on functionals under first order degeneracy
Q. Chen and Z. Fang · 2018
Closest in time.
Interpolating between optimal transport and MMD using sinkhorn divergences
J. Feydy, T. Séjourné, F.-X. Vialard, S.-I. Amari, A. Trouvé, and G. Peyré · 2018
Closest in time.
Sample complexity of sinkhorn divergences
A. Genevay, L. Chizat, F. Bach, M. Cuturi, and G. Peyré · 2018
Closest in time.
Empirical regularized optimal transport: Statistical theory and applications
M. Klatt, C. Tameling, and A. Munk · 2018
Closest in time.
Differential properties of Sinkhorn approximation for learning with Wasserstein distance
G. Luise, A. Rudi, M. Pontil, and C. Ciliberto · 2018
Closest in time.