Fetching the paper…
Reading the bibliography…
Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space.
Some Theorems on Distribution Functions
H. Cramér et H. Wold · 1936
Earlier work this paper cites.
Assignment problems and the location of economic activities
Tjalling Koopmans and Martin J. Beckmann · 1957
Earlier work this paper cites.
Convex analysis
R. Tyrrell Rockafellar · 1970
Earlier work this paper cites.
Linear Complementarity, Linear and Nonlinear Programming
K.G. Murty · 1988
Earlier work this paper cites.
The kernel trick for distances
Bernhard Schölkopf · 2001
Earlier work this paper cites.
A survey of the quadratic assignment problem
Eliane Loiola, Nair Abreu, Paulo Boaventura-Netto, Peter Hahn, and Tania Querido · 2007
Earlier work this paper cites.
Optimal Transport: Old and New
Cédric Villani · 2008
Earlier work this paper cites.
Optimization algorithms on matrix manifolds
P-A Absil, Robert Mahony, and Rodolphe Sepulchre · 2009
Earlier work this paper cites.
Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2011
Earlier work this paper cites.
Gromov wasserstein distances and the metric approach to object matching
Facundo Memoli · 2011
Earlier work this paper cites.
The Wiener maximum quadratic assignment problem
Eranda Çela, Nina S. Schmuck, Shmuel Wimer, and Gerhard J. Woeginger · 2011
Earlier work this paper cites.
The space of spaces: curvature bounds and gradient flows on the space of metric measure spaces
Karl-Theodor Sturm · 2012
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Earlier work this paper cites.
Unidimensional and Evolution Methods for Optimal Transportation
Nicolas Bonnotte · 2013
Earlier work this paper cites.
The Quadratic Assignment Problem: Theory and Algorithms
Eranda Çela · 2013
Earlier work this paper cites.
Map-based exploration of intrinsic shape differences and variability
Raif M Rustamov, Maks Ovsjanikov, Omri Azencot, Mirela Ben-Chen, Frédéric Chazal, and Leonidas Guibas · 2013
Earlier work this paper cites.
Sliced and Radon Wasserstein Barycenters of Measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
Earlier work this paper cites.
Well-solvable cases of the QAP with block-structured matrices
Eranda Çela, Vladimir G. Deineko, and Gerhard J. Woeginger · 2015
Earlier work this paper cites.
Autograd: Effortless gradients in numpy
Dougal Maclaurin, David Duvenaud, and Ryan P Adams · 2015
Cited alongside, same era.
Wasserstein barycentric coordinates: histogram regression using optimal transport
Nicolas Bonneel, Gabriel Peyré, and Marco Cuturi · 2016
Cited alongside, same era.
Supervised word mover’s distance
G. Huang, C. Guo, M. Kusner, Y. Sun, F. Sha, and K. Weinberger · 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.
Sliced wasserstein kernels for probability distributions
Soheil Kolouri, Yang Zou, and Gustavo K. Rohde · 2016
Cited alongside, same era.
Entropic metric alignment for correspondence problems
Justin Solomon, Gabriel Peyré, Vladimir G. Kim, and Suvrit Sra · 2016
Cited alongside, same era.
Gromov-wasserstein alignment of word embedding spaces
David Alvarez-Melis and Tommi S Jaakkola · 2018
Later among the works it cites.
New special cases of the Quadratic Assignment Problem with diagonally structured coefficient matrices
Eranda Çela, Vladimir Deineko, and Gerhard J. Woeginger · 2018
Later among the works it cites.
Gromov-Monge quasi-metrics and distance distributions
Facundo Mémoli and Tom Needham · 2018
Later among the works it cites.
(probably) concave graph matching
Haggai Maron and Yaron Lipman · 2018
Later among the works it cites.
Kernel operations on the gpu, with autodiff, without memory overflows
Benjamin Charlier, Jean Feydy, and Joan Glaunes · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gromov-Wasserstein Averaging of Kernel and Distance Matrices
Gabriel Peyré, Marco Cuturi, and Justin Solomon · 2016
Cited alongside, same era.
Pymanopt: A python toolbox for optimization on manifolds using automatic differentiation
James Townsend, Niklas Koep, and Sebastian Weichwald · 2016
Cited alongside, same era.
Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems
Bernhard Schmitzer · 2016
Cited alongside, same era.
A transportation l p l^{p} distance for signal analysis
Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, and Dejan Slepčev · 2017
Cited alongside, same era.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2017
Cited alongside, same era.
Mayank Meghwanshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, and Bamdev Mishra · 2018
Later among the works it cites.
Optimal Transport for structured data with application on graphs
Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, and Nicolas Courty · 2019
Closest in time.
Learning Generative Models across Incomparable Spaces
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, and Stefanie Jegelka · 2019
Closest in time.
Sliced wasserstein auto-encoders
Soheil Kolouri, Phillip E. Pope, Charles E. Martin, and Gustavo K. Rohde · 2019
Closest in time.
Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, and Fabian-Robert Stöter · 2019
Closest in time.
Sliced wasserstein generative models
Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, and Luc Van Gool · 2019
Closest in time.
Towards optimal transport with global invariances
David Alvarez-Melis, Stefanie Jegelka, and Tommi S. Jaakkola · 2019
Closest in time.
Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
Closest in time.
Subspace robust Wasserstein distances
François-Pierre Paty and Marco Cuturi · 2019
Closest in time.
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
Closest in time.
On Assignment Problems Related to Gromov-Wasserstein Distances on the Real Line
Robert Beinert, Cosmas Heiss, and Gabriele Steidl · 2022
Closest in time.
On the existence of Monge maps for the Gromov-Wasserstein distance
Theo Dumont, Théo Lacombe, and François-Xavier Vialard · 2022
Closest in time.