34 citations · 65 across the 7 of their papers we have counts for
6 papers · 1 filter
Minibatch optimal transport distances; analysis and applications
Kilian Fatras, Younes Zine, Szymon Majewski +3
Optimal transport distances have become a classic tool to compare probability distributions and have found many applications in machine learning. Yet, despite recent algorithmic de…
CO-Optimal Transport
Ievgen Redko, Titouan Vayer, Rémi Flamary +1
Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions. Yet, its original formulation…
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…
Optimal Transport for Multi-source Domain Adaptation under Target Shift
Ievgen Redko, Nicolas Courty, Rémi Flamary +1
In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target sh…
Learning Wasserstein Embeddings
Nicolas Courty, Rémi Flamary, Mélanie Ducoffe
The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numer…