136 citations · 211 across the 14 of their papers we have counts for
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Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics
Guillaume Mahey, Laetitia Chapel, Gilles Gasso +2
Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake. In this paper, we propose…
Partial Optimal Transport with Applications on Positive-Unlabeled Learning
Laetitia Chapel, Mokhtar Z. Alaya, Gilles Gasso
Classical optimal transport problem seeks a transportation map that preserves the total mass betwenn two probability distributions, requiring their mass to be the same. This may be…
Time Series Alignment with Global Invariances
Titouan Vayer, Romain Tavenard, Laetitia Chapel +3
Multivariate time series are ubiquitous objects in signal processing. Measuring a distance or similarity between two such objects is of prime interest in a variety of applications,…
Sliced Gromov-Wasserstein
Titouan Vayer, Rémi Flamary, Romain Tavenard +2
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…
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Titouan Vayer, Laetita Chapel, Rémi Flamary +2
Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions.…
Optimal Transport for structured data with application on graphs
Titouan Vayer, Laetitia Chapel, Rémi Flamary +2
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space. We consid…