72 citations · 127 across the 16 of their papers we have counts for
5 papers · 1 filter
Gauss-Newton drifting extends natural gradient descent
Théo Dumont, Théo Lacombe, François-Xavier Vialard
Drifting methods have recently been introduced as a promising new paradigm for training generative models, and have attracted a lot of attention so far. In this work, we focus on h…
Learning Monge maps with constrained drifting models
Théo Dumont, Théo Lacombe, François-Xavier Vialard
We study the estimation of optimal transport (OT) maps between an arbitrary source probability measure and a log-concave target probability measure. Our contributions are twofold.…
Persistence-based topological optimization: a survey
Mathieu Carriere, Yuichi Ike, Théo Lacombe +1
Computational topology provides a tool, persistent homology, to extract quantitative descriptors from structured objects (images, graphs, point clouds, etc). These descriptors can…
The Wasserstein gradient flow of the Sinkhorn divergence between Gaussian distributions
Mathis Hardion, Théo Lacombe
We study the Wasserstein gradient flow of the Sinkhorn divergence when both the source and the target are Gaussian distributions. We prove the existence of a flow that stays in the…
Revisiting the Sliced Wasserstein Kernel for persistence diagrams: a Figalli-Gigli approach
Marc Janthial, Théo Lacombe
The Sliced Wasserstein Kernel (SWK) for persistence diagrams was introduced in (Carri{è}re et al. 2017) as a powerful tool to implicitly embed persistence diagrams in a Hilbert spa…