7 papers · 1 filter
Wasserstein Spatial Depth
François Bachoc, Alberto González-Sanz, Jean-Michel Loubes +1
Modeling observations as random distributions embedded within Wasserstein spaces is becoming increasingly popular across scientific fields, as it captures the variability and geome…
Scale estimation and rate-unbiasedness for Gaussian processes under smoothness misspecification
Toni Karvonen, François Bachoc
Gaussian process regression is used throughout statistics and machine learning for prediction and uncertainty quantification. A Gaussian process is specified by its mean and covari…
Kriging measure-valued data with sparse observations: application to nuclear safety studies
Florian Gossard, François Bachoc, Jean Baccou +3
This work addresses the interpolation of probability measures within a spatial statistics framework. We develop a Kriging approach in the Wasserstein space, leveraging the quantile…
Improved learning theory for kernel distribution regression with two-stage sampling
François Bachoc, Louis Béthune, Alberto González-Sanz +1
The distribution regression problem encompasses many important statistics and machine learning tasks, and arises in a large range of applications. Among various existing approaches…
Contraction rates and projection subspace estimation with Gaussian process priors in high dimension
Elie Odin, François Bachoc, Agnès Lagnoux
This work explores the dimension reduction problem for Bayesian nonparametric regression and density estimation. More precisely, we are interested in estimating a functional parame…
Inference post region selection
Dominique Bontemps, François Bachoc, Pierre Neuvial
Post-selection inference consists in providing statistical guarantees, based on a data set, that are robust to a prior model selection step on the same data set. In this paper, we…