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09/04/2023
Neural network-based emulation of interstellar medium models
The interpretation of observations of atomic and molecular tracers in th...
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05/20/2023
Normalizing flow sampling with Langevin dynamics in the latent space
Normalizing flows (NF) use a continuous generator to map a simple latent...
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04/21/2023
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
This paper introduces a stochastic plug-and-play (PnP) sampling algorith...
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11/23/2022
Efficient sampling of non log-concave posterior distributions with mixture of noises
This paper focuses on a challenging class of inverse problems that is of...
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10/05/2022
A distributed Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems
Sampling-based algorithms are classical approaches to perform Bayesian i...
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07/12/2022
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
Despite their advantages, normalizing flows generally suffer from severa...
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07/04/2022
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
Optimal transport (OT) provides effective tools for comparing and mappin...
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10/04/2020
High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
Efficient sampling from a high-dimensional Gaussian distribution is an o...
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02/22/2020
Kernel interpolation with continuous volume sampling
A fundamental task in kernel methods is to pick nodes and weights, so as...
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06/18/2019
Kernel quadrature with DPPs
We study quadrature rules for functions living in an RKHS, using nodes s...
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02/15/2019
Asymptotically exact data augmentation: models, properties and algorithms
Data augmentation, by the introduction of auxiliary variables, has becom...
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12/23/2018
A determinantal point process for column subset selection
Dimensionality reduction is a first step of many machine learning pipeli...
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04/16/2018
Split-and-augmented Gibbs sampler - Application to large-scale inference problems
Recently, a new class of Markov chain Monte Carlo (MCMC) algorithms took...
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09/17/2017
Bayesian nonparametric Principal Component Analysis
Principal component analysis (PCA) is very popular to perform dimension ...
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12/18/2015
Bayesian anti-sparse coding
Sparse representations have proven their efficiency in solving a wide cl...
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04/12/2013