François Bachoc
François Bachoc
Assistant professor (tenured), Toulouse Mathematics Institute
Verified email at - Homepage
Cited by
Cited by
Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
F Bachoc
Computational Statistics & Data Analysis 66, 55-69, 2013
Finite-dimensional Gaussian approximation with linear inequality constraints
AF López-Lopera, F Bachoc, N Durrande, O Roustant
SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1224-1255, 2018
A supermartingale approach to Gaussian process based sequential design of experiments
J Bect, F Bachoc, D Ginsbourger
Nested Kriging predictions for datasets with a large number of observations
D Rullière, N Durrande, F Bachoc, C Chevalier
Statistics and Computing 28, 849-867, 2018
Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes
F Bachoc
Journal of Multivariate Analysis 125, 1-35, 2014
A Gaussian process regression model for distribution inputs
F Bachoc, F Gamboa, JM Loubes, N Venet
IEEE Transactions on Information Theory 64 (10), 6620-6637, 2017
Uniformly valid confidence intervals post-model-selection
F Bachoc, D Preinerstorfer, L Steinberger
The Annals of Statistics 48 (1), 440-463, 2020
Valid confidence intervals for post-model-selection predictors
F Bachoc, H Leeb, BM Pötscher
The Annals of Statistics 47 (3), 1475-1504, 2019
Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution
B Broto, F Bachoc, M Depecker
SIAM/ASA Journal on Uncertainty Quantification 8 (2), 693-716, 2020
Calibration and improved prediction of computer models by universal Kriging
F Bachoc, G Bois, J Garnier, JM Martinez
Nuclear Science and Engineering 176 (1), 81-97, 2014
Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments
F Bachoc
Université Paris-Diderot-Paris VII, 2013
Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case
F Bachoc
Gaussian process metamodeling of functional-input code for coastal flood hazard assessment
J Betancourt, F Bachoc, T Klein, D Idier, R Pedreros, J Rohmer
Reliability Engineering & System Safety 198, 106870, 2020
Gaussian process optimization with failures: classification and convergence proof
F Bachoc, C Helbert, V Picheny
Journal of Global Optimization 78 (3), 483-506, 2020
Maximum likelihood estimation for Gaussian processes under inequality constraints
F Bachoc, A Lagnoux, AF López-Lopera
Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding
F Bachoc, A Suvorikova, D Ginsbourger, JM Loubes, V Spokoiny
Ithaca, NY: Cornell University Library, 2020
Asymptotic properties of multivariate tapering for estimation and prediction
R Furrer, F Bachoc, J Du
Journal of Multivariate Analysis 149, 177-191, 2016
Sensitivity indices for independent groups of variables
B Broto, F Bachoc, M Depecker, JM Martinez
Mathematics and Computers in Simulation 163, 19-31, 2019
Estimation paramétrique de la fonction de covariance dans le modèle de krigeage par processus gaussiens: application à la quantification des incertitudes en simulation numérique
F Bachoc
Paris 7, 2013
Spatial blind source separation
F Bachoc, MG Genton, K Nordhausen, A Ruiz-Gazen, J Virta
Biometrika 107 (3), 627-646, 2020
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