93 citations · 107 across the 6 of their papers we have counts for
6 papers
Example-Based Sampling with Diffusion Models
Bastien Doignies, Nicolas Bonneel, David Coeurjolly +4
Much effort has been put into developing samplers with specific properties, such as producing blue noise, low-discrepancy, lattice or Poisson disk samples. These samplers can be sl…
Generator Matrices by Solving Integer Linear Programs
Loïs Paulin, David Coeurjolly, Nicolas Bonneel +3
In quasi-Monte Carlo methods, generating high-dimensional low discrepancy sequences by generator matrices is a popular and efficient approach. Historically, constructing or finding…
Symmetrized semi-discrete optimal transport
Agathe Herrou, Bruno Lévy, Vincent Nivoliers +2
Interpolating between measures supported by polygonal or polyhedral domains is a problem that has been recently addressed by the semi-discrete optimal transport framework. Within t…
Learning to Generate Wasserstein Barycenters
Julien Lacombe, Julie Digne, Nicolas Courty +1
Optimal transport is a notoriously difficult problem to solve numerically, with current approaches often remaining intractable for very large scale applications such as those encou…
Ground Metric Learning on Graphs
Matthieu Heitz, Nicolas Bonneel, David Coeurjolly +2
Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications…
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
Morgan A. Schmitz, Matthieu Heitz, Nicolas Bonneel +5
This paper introduces a new nonlinear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim…