Publications (60)
Large-Scale Optimal Transport and Mapping Estimation
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary +3
This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) pla…
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,…
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…
ABM: Alignment-Aware Bridge Matching for Image-to-Image Translation
Aimi Okabayashi, Georges Le Bellier, Nicolas Audebert +3
Paired image-to-image translation underpins a wide range of computer vision tasks, including image editing, sensor translation, and domain adaptation. Bridge matching and flow matc…
Subspace Detours Meet Gromov-Wasserstein
Clément Bonet, Nicolas Courty, François Septier +1
In the context of optimal transport methods, the subspace detour approach was recently presented by Muzellec and Cuturi (2019). It consists in building a nearly optimal transport p…
Semi-relaxed Gromov-Wasserstein divergence with applications on graphs
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli +2
Comparing structured objects such as graphs is a fundamental operation involved in many learning tasks. To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transpor…