most citedPointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior

1 citations · 1 across the 2 of their papers we have counts for

collaborators

3 papers

stat.ML2024

A variational Bayes approach to inference for low-dimensional parameters in high-dimensional linear regression

Ismaël Castillo, Alice L'Huillier, Kolyan Ray +1

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…

math.ST2023★ 1 cited

Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior

Luke Travis, Kolyan Ray

We study pointwise estimation and uncertainty quantification for a sparse variational Gaussian process method with eigenvector inducing variables. For a rescaled Brownian motion pr…

math.ST2023

Semiparametric inference using fractional posteriors

Alice L'Huillier, Luke Travis, Ismaël Castillo +1

We establish a general Bernstein--von Mises theorem for approximately linear semiparametric functionals of fractional posterior distributions based on nonparametric priors. This is…