Rémi Flamary
Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy
Neural Information Processing Systems http://nips.cc/
Sliced Gromov-Wasserstein
Vayer Titouan, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel
Neural Information Processing Systems http://nips.cc/
Sliced Gromov-Wasserstein
Vayer Titouan, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel
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 space. However, this Optimal Transport (OT) distance requires solving a complex non convex quadratic program which is most of the time very costly both in time and memory. Contrary to GW, the Wasserstein distance (W) enjoys several properties (e.g.
Optimal spectral transportation with application to music transcription
Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya
Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates.
Mapping Estimation for Discrete Optimal Transport
Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard
It has been proven useful in several settings.
Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy
Neural Information Processing Systems http://nips.cc/