17 citations · 32 across the 10 of their papers we have counts for
14 papers
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling
Julien Demange-Chryst, François Bachoc, Jérôme Morio +1
Probability density function estimation with weighted samples is the main foundation of all adaptive importance sampling algorithms. Classically, a target distribution is approxima…
Efficient estimation of multiple expectations with the same sample by adaptive importance sampling and control variates
Julien Demange-Chryst, François Bachoc, Jérôme Morio
Some classical uncertainty quantification problems require the estimation of multiple expectations. Estimating all of them accurately is crucial and can have a major impact on the…
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication
François Bachoc, Agnès Lagnoux
We provide posterior contraction rates for constrained deep Gaussian processes in non-parametric density estimation and classication. The constraints are in the form of bounds on t…
Asymptotic analysis of maximum likelihood estimation of covariance parameters for Gaussian processes: an introduction with proofs
François Bachoc
This article provides an introduction to the asymptotic analysis of covariance parameter estimation for Gaussian processes. Maximum likelihood estimation is considered. The aim of…
Rate of convergence for geometric inference based on the empirical Christoffel function
Mai Trang Vu, François Bachoc, Edouard Pauwels
We consider the problem of estimating the support of a measure from a finite, independent, sample. The estimators which are considered are constructed based on the empirical Christ…
Composite likelihood estimation for a gaussian process under fixed domain asymptotics
François Bachoc, Moreno Bevilacqua, Daira Velandia
We study the problem of estimating the covariance parameters of a one-dimensional Gaussian process with exponential covariance function under fixed-domain asymptotics. We show that…
On the Post Selection Inference constant under Restricted Isometry Properties
François Bachoc, Gilles Blanchard, Pierre Neuvial
Uniformly valid confidence intervals post model selection in regression can be constructed based on Post-Selection Inference (PoSI) constants. PoSI constants are minimal for orthog…
Cross-validation estimation of covariance parameters under fixed-domain asymptotics
Francois Bachoc, Agnes Lagnoux, Thi Mong Ngoc Nguyen
We consider a one-dimensional Gaussian process having exponential covariance function. Under fixed-domain asymptotics, we prove the strong consistency and asymptotic normality of a…
On the smallest eigenvalues of covariance matrices of multivariate spatial processes
François Bachoc, Reinhard Furrer
There has been a growing interest in providing models for multivariate spatial processes. A majority of these models specify a parametric matrix covariance function. Based on obser…
Asymptotic properties of multivariate tapering for estimation and prediction
R. Furrer, F. Bachoc, J. Du
Parameter estimation for and prediction of spatially or spatio--temporally correlated random processes are used in many areas and often require the solution of a large linear syste…
Optimal configurations of lines and a statistical application
François Bachoc, Martin Ehler, Manuel Gräf
Motivated by the construction of confidence intervals in statistics, we study optimal configurations of lines in real projective space . For small , we determi…
Valid confidence intervals for post-model-selection predictors
François Bachoc, Hannes Leeb, Benedikt M. Pötscher
We consider inference post-model-selection in linear regression. In this setting, Berk et al.(2013) recently introduced a class of confidence sets, the so-called PoSI intervals, th…
Hastings-Metropolis algorithm on Markov chains for small-probability estimation
François Bachoc, Lionel Lenôtre, Achref Bachouch
Shielding studies in neutron transport, with Monte Carlo codes, yield challenging problems of small-probability estimation. The particularity of these studies is that the small pro…
Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes
François Bachoc
Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the p…