58 citations · 304 across the 27 of their papers we have counts for
10 papers · 1 filter
Signal reconstruction using determinantal sampling
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
We study the approximation of a square-integrable function from a finite number of evaluations on a random set of nodes according to a well-chosen distribution. This is particularl…
Normalizing flow sampling with Langevin dynamics in the latent space
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Normalizing flows (NF) use a continuous generator to map a simple latent (e.g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.…
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based…
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e.g., images) and their failing to det…
Kernel interpolation with continuous volume sampling
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
A fundamental task in kernel methods is to pick nodes and weights, so as to approximate a given function from an RKHS by the weighted sum of kernel translates located at the nodes.…
Kernel quadrature with DPPs
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
We study quadrature rules for functions from an RKHS, using nodes sampled from a determinantal point process (DPP). DPPs are parametrized by a kernel, and we use a truncated and sa…