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The Gromov-Wasserstein (GW) distance quantifies discrepancy between metric measure spaces and provides a natural framework for aligning heterogeneous datasets.
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Functional analysis, Sobolev spaces and partial differential equations , volume 2
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Gromov-Wasserstein distances and the metric approach to object matching
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M. Cuturi · 2013
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Local minimization, variational evolution and Γ \Gamma -convergence , volume 2094
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O. Devolder, F. Glineur, and Y. Nesterov · 2014
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F. Santambrogio · 2015
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POT: Python optimal transport
R. Flamary, N. Courty, A. Gramfort, M. Z. Alaya, A. Boisbunon, S. Chambon, L. Chapel, A. Corenflos, K. Fatras, N. Fournier, L. Gautheron, N. T. Gayraud, H. Janati, A. Rakotomamonjy, I. Redko, A. Rolet, A. Schutz, V. Seguy, D. J. Sutherland, R. Tavenard, A. Tong, and T. Vayer · 2021
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Introduction to entropic optimal transport
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Accelerated gradient methods for nonconvex nonlinear and stochastic programming
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Gromov-Wasserstein averaging of kernel and distance matrices
G. Peyré, M. Cuturi, and J. Solomon · 2016
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Entropic metric alignment for correspondence problems
J. Solomon, G. Peyré, V. G. Kim, and S. Sra · 2016
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Convergence of entropic schemes for optimal transport and gradient flows
G. Carlier, V. Duval, G. Peyré, and B. Schmitzer · 2017
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Gradient method with inexact oracle for composite non-convex optimization
P. Dvurechensky · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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The unbalanced Gromov-Wasserstein distance: conic formulation and relaxation
T. Séjourné, F.-X. Vialard, and G. Peyré · 2021
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Understanding the acceleration phenomenon via high-resolution differential equations
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On assignment problems related to Gromov-Wasserstein distances on the real line
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Gradient norm minimization of Nesterov acceleration: o ( 1 / k 3 ) o(1/k^{3})
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Gromov-Wasserstein distances between Gaussian distributions
J. Delon, A. Desolneux, and A. Salmona · 2022
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SCOT: single-cell multi-omics alignment with optimal transport
P. Demetci, R. Santorella, B. Sandstede, W. S. Noble, and R. Singh · 2022
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On the existence of Monge maps for the Gromov-Wasserstein distance
T. Dumont, T. Lacombe, and F.-X. Vialard · 2022
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Quantitative stability of regularized optimal transport and convergence of Sinkhorn’s algorithm
S. Eckstein and M. Nutz · 2022
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Stability of entropic optimal transport and Schrödinger bridges
P. Ghosal, M. Nutz, and E. Bernton · 2022
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Entropic Gromov-Wasserstein between Gaussian distributions
K. Le, D. Q. Le, H. Nguyen, D. Do, T. Pham, and N. Ho · 2022
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Linear-time Gromov- Wasserstein distances using low rank couplings and costs
M. Scetbon, G. Peyré, and M. Cuturi · 2022
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Semi-relaxed Gromov Wasserstein divergence with applications on graphs
C. Vincent-Cuaz, R. Flamary, M. Corneli, T. Vayer, and N. Courty · 2022
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Semidefinite relaxations of the gromov-wasserstein distance
J. Chen, B. T. Nguyen, and Y. S. Soh · 2023
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Computing the Gromov-Wasserstein distance between two surface meshes using optimal transport
P. Koehl, M. Delarue, and H. Orland · 2023
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Stability of Schrödinger potentials and convergence of Sinkhorn’s algorithm
M. Nutz and J. Wiesel · 2023
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