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Patrice Koehl, Marc Delarue and Henri Orland
The Gromov-Wasserstein (GW) formalism can be seen as a generalization of the optimal transport (OT) formalism for comparing two distributions associated with different metric spaces. It is a quadratic optimization problem and solving it usually has compu...
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Clément Bonet, Titouan Vayer, Nicolas Courty, François Septier and Lucas Drumetz
In the context of optimal transport (OT) methods, the subspace detour approach was recently proposed by Muzellec and Cuturi. It consists of first finding an optimal plan between the measures projected on a wisely chosen subspace and then completing it in...
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Titouan Vayer, Laetitia Chapel, Remi Flamary, Romain Tavenard and Nicolas Courty
Optimal transport theory has recently found many applications in machine learning thanks to its capacity to meaningfully compare various machine learning objects that are viewed as distributions. The Kantorovitch formulation, leading to the Wasserstein d...
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