no code implementations • 12 Jun 2023 • Thibault Séjourné, Clément Bonet, Kilian Fatras, Kimia Nadjahi, Nicolas Courty
In parallel, unbalanced OT was designed to allow comparisons of more general positive measures, while being more robust to outliers.
no code implementations • 16 Nov 2022 • Thibault Séjourné, Gabriel Peyré, François-Xavier Vialard
Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions.
no code implementations • 3 Jan 2022 • Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré
In this work, we identify the cause for this deficiency, namely the lack of a global normalization of the iterates, which equivalently corresponds to a translation of the dual OT potentials.
2 code implementations • 5 Mar 2021 • Kilian Fatras, Thibault Séjourné, Nicolas Courty, Rémi Flamary
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions.
2 code implementations • NeurIPS 2021 • Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré
The GW distance is however limited to the comparison of metric measure spaces endowed with a probability distribution.
4 code implementations • 28 Oct 2019 • Thibault Séjourné, Jean Feydy, François-Xavier Vialard, Alain Trouvé, Gabriel Peyré
Optimal transport induces the Earth Mover's (Wasserstein) distance between probability distributions, a geometric divergence that is relevant to a wide range of problems.
1 code implementation • 18 Oct 2018 • Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, Gabriel Peyré
Comparing probability distributions is a fundamental problem in data sciences.
Statistics Theory Statistics Theory 62