Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification. - François Bachoc (Comput. Stat. Data Anal., 2013)

Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes. - François Bachoc (J. Multivar. Anal., 2014)

Asymptotic properties of multivariate tapering for estimation and prediction. - Reinhard Furrer; François Bachoc; Juan Du (J. Multivar. Anal., 2016)

Optimal configurations of lines and a statistical application. - François Bachoc; Martin Ehler; Manuel Gräf (Adv. Comput. Math., 2017)

Cross-validation estimation of covariance parameters under fixed-domain asymptotics. - François Bachoc; Agnès Lagnoux; Thi Mong Ngoc Nguyen (J. Multivar. Anal., 2017)

Entropic Variable Boosting for Explainability and Interpretability in Machine Learning. - François Bachoc; Fabrice Gamboa; Jean-Michel Loubes; Laurent Risser (CoRR, 2018)

Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints. - Andrés F. López-Lopera; François Bachoc; Nicolas Durrande; Olivier Roustant (SIAM/ASA J. Uncertain. Quantification, 2018)

Nested Kriging predictions for datasets with a large number of observations. - Didier Rullière; Nicolas Durrande; François Bachoc; Clément Chevalier (Stat. Comput., 2018)

A Gaussian Process Regression Model for Distribution Inputs. - François Bachoc; Fabrice Gamboa; Jean-Michel Loubes; Nil Venet (IEEE Trans. Inf. Theory, 2018)

Learning a Gaussian Process Model on the Riemannian Manifold of Non-decreasing Distribution Functions. - Chafik Samir; Jean-Michel Loubes; Anne-Françoise Yao; François Bachoc (PRICAI (2), 2019)

Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC. - Andrés F. López-Lopera; François Bachoc; Nicolas Durrande; Olivier Roustant (CoRR, 2019)

Rate of convergence for geometric inference based on the empirical Christoffel function. - Mai Trang Vu; François Bachoc; Edouard Pauwels (CoRR, 2019)

Gaussian Process-Based Dimension Reduction for Goal-Oriented Sequential Design. - Malek Ben Salem 002; François Bachoc; Olivier Roustant; Fabrice Gamboa; Lionel Tomaso (SIAM/ASA J. Uncertain. Quantification, 2019)

Composite likelihood estimation for a Gaussian process under fixed domain asymptotics. - François Bachoc; Moreno Bevilacqua; Daira Velandia (J. Multivar. Anal., 2019)

Sensitivity indices for independent groups of variables. - Baptiste Broto; François Bachoc; Marine Depecker; Jean-Marc Martinez (Math. Comput. Simul., 2019)

Multi-Output Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment. - Andrés F. López-Lopera; Déborah Idier; Jérémy Rohmer 001; François Bachoc (CoRR, 2020)

Gaussian process optimization with failures: classification and convergence proof. - François Bachoc; Céline Helbert; Victor Picheny (J. Glob. Optim., 2020)

Variance Reduction for Estimation of Shapley Effects and Adaptation to Unknown Input Distribution. - Baptiste Broto; François Bachoc; Marine Depecker (SIAM/ASA J. Uncertain. Quantification, 2020)

Gaussian process metamodeling of functional-input code for coastal flood hazard assessment. - José Betancourt; François Bachoc; Thierry Klein; Déborah Idier; Rodrigo Pedreros; Jérémy Rohmer 001 (Reliab. Eng. Syst. Saf., 2020)

The Sample Complexity of Level Set Approximation. - François Bachoc; Tommaso Cesari; Sébastien Gerchinovitz (AISTATS, 2021)

Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates. - François Bachoc; Tommaso Cesari; Sébastien Gerchinovitz (NeurIPS, 2021)

Bayesian regression and classification using Gaussian process priors indexed by probability density functions. - Anis Fradi; Yan Feunteun; Chafik Samir; M. Baklouti; François Bachoc; Jean-Michel Loubes (Inf. Sci., 2021)

Gaussian Linear Approximation for the Estimation of the Shapley Effects. - Baptiste Broto; François Bachoc; Marine Depecker; Jean-Marc Martinez (SIAM/ASA J. Uncertain. Quantification, 2021)

Local Identifiability of Deep ReLU Neural Networks: the Theory. - Joachim Bona-Pellissier; François Malgouyres; François Bachoc (NeurIPS, 2022)

High-dimensional Additive Gaussian Processes under Monotonicity Constraints. - Andrés F. López-Lopera; François Bachoc; Olivier Roustant (NeurIPS, 2022)

A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization. - François Bachoc; Tommaso Cesari; Roberto Colomboni; Andrea Paudice (CoRR, 2022)

Regret Analysis of Dyadic Search. - François Bachoc; Tommaso Cesari; Roberto Colomboni; Andrea Paudice (CoRR, 2022)

Block-Diagonal Covariance Estimation and Application to the Shapley Effects in Sensitivity Analysis. - Baptiste Broto; François Bachoc; Laura Clouvel; Jean-Marc Martinez (SIAM/ASA J. Uncertain. Quantification, 2022)

Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment. - Andrés F. López-Lopera; Déborah Idier; Jérémy Rohmer 001; François Bachoc (Reliab. Eng. Syst. Saf., 2022)

Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints. - François Bachoc; Andrés F. López-Lopera; Olivier Roustant (SIAM J. Math. Data Sci., 2022)

Gaussian Processes on Distributions based on Regularized Optimal Transport. - François Bachoc; Louis Béthune; Alberto González-Sanz; Jean-Michel Loubes (AISTATS, 2023)

Efficient estimation of multiple expectations with the same sample by adaptive importance sampling and control variates. - Julien Demange-Chryst; François Bachoc; Jérôme Morio (Stat. Comput., 2023)

Parameter identifiability of a deep feedforward ReLU neural network. - Joachim Bona-Pellissier; François Bachoc; François Malgouyres (Mach. Learn., 2023)

Fair Online Bilateral Trade. - François Bachoc; Nicolò Cesa-Bianchi; Tommaso Cesari; Roberto Colomboni (NeurIPS, 2024)

Geometry-induced Implicit Regularization in Deep ReLU Neural Networks. - Joachim Bona-Pellissier; François Malgouyres; François Bachoc (CoRR, 2024)

A Contextual Online Learning Theory of Brokerage. - François Bachoc; Tommaso Cesari; Roberto Colomboni (CoRR, 2024)

funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs. - José Betancourt; François Bachoc; Thierry Klein; Déborah Idier; Jérémy Rohmer 001; Yves Deville (J. Stat. Softw., 2024)

A Theoretical Framework for Zeroth-Order Budget Convex Optimization. - François Bachoc; Tommaso Cesari; Roberto Colomboni; Andrea Paudice (Trans. Mach. Learn. Res., 2024)

Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling. - Julien Demange-Chryst; François Bachoc; Jérôme Morio; Timothé Krauth (Trans. Mach. Learn. Res., 2024)

A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage. - François Bachoc; Tommaso Cesari; Roberto Colomboni (AISTATS, 2025)

Covariance parameter estimation of Gaussian processes with approximated functional inputs. - Lucas Reding; Andrés F. López-Lopera; François Bachoc (J. Multivar. Anal., 2025)

When majority rules, minority loses: bias amplification of gradient descent. - François Bachoc; Jérôme Bolte; Ryan Boustany; Jean-Michel Loubes (CoRR, 2025)