4 papers
Multiple testing with the horseshoe
Sayantan Banerjee, Ismaël Castillo, Fanny Villers
We study multiple testing under continuous global--local shrinkage priors, with a focus on the horseshoe prior in high-dimensional sparse settings. While such priors provide adapti…
Bayesian inference in high-dimensional models
Sayantan Banerjee, Ismaël Castillo, Subhashis Ghosal
Models with dimension more than the available sample size are now commonly used in various applications. A sensible inference is possible using a lower-dimensional structure. In re…
A variational Bayes approach to inference for low-dimensional parameters in high-dimensional linear regression
Ismaël Castillo, Ismaël Castillo, Alice L'Huillier +2
We propose a scalable variational Bayes method for statistical inference for a single or pre-specified low-dimensional subset of the coordinates of a high-dimensional parameter in…
Deep Horseshoe Gaussian Processes
Ismaël Castillo, Thibault Randrianarisoa
Deep Gaussian processes have recently been proposed as natural objects to fit, similarly to deep neural networks, possibly complex features present in modern data samples, such as…