François Bachoc

CoRR, 2024

2023

Parameter identifiability of a deep feedforward ReLU neural network.

[BibT_eX]

[DOI]

Joachim Bona-Pellissier

François Malgouyres

Mach. Learn., November, 2023

Efficient estimation of multiple expectations with the same sample by adaptive importance sampling and control variates.

[BibT_eX]

[DOI]

Julien Demange-Chryst

Jérôme Morio

Stat. Comput., October, 2023

Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling.

[BibT_eX]

[DOI]

Julien Demange-Chryst

Jérôme Morio

Timothé Krauth

CoRR, 2023

Gaussian Processes on Distributions based on Regularized Optimal Transport.

[BibT_eX]

[DOI]

Louis Béthune

Alberto González-Sanz

Jean-Michel Loubes

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., June, 2022

Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment.

[BibT_eX]

[DOI]

Déborah Idier

Jérémy Rohmer

Reliab. Eng. Syst. Saf., 2022

Block-Diagonal Covariance Estimation and Application to the Shapley Effects in Sensitivity Analysis.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2022

Regret Analysis of Dyadic Search.

[BibT_eX]

[DOI]

CoRR, 2022

A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization.

[BibT_eX]

[DOI]

CoRR, 2022

High-dimensional Additive Gaussian Processes under Monotonicity Constraints.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Local Identifiability of Deep ReLU Neural Networks: the Theory.

[BibT_eX]

[DOI]

Joachim Bona-Pellissier

François Malgouyres

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Gaussian Linear Approximation for the Estimation of the Shapley Effects.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2021

Bayesian regression and classification using Gaussian process priors indexed by probability density functions.

[BibT_eX]

[DOI]

Inf. Sci., 2021

Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates.

[BibT_eX]

[DOI]

Tommaso Cesari

Sébastien Gerchinovitz

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Sample Complexity of Level Set Approximation.

[BibT_eX]

[DOI]

Tommaso Cesari

Sébastien Gerchinovitz

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Gaussian process metamodeling of functional-input code for coastal flood hazard assessment.

[BibT_eX]

[DOI]

Reliab. Eng. Syst. Saf., 2020

Variance Reduction for Estimation of Shapley Effects and Adaptation to Unknown Input Distribution.

[BibT_eX]

[DOI]

Baptiste Broto

Marine Depecker

SIAM/ASA J. Uncertain. Quantification, 2020

Gaussian process optimization with failures: classification and convergence proof.

[BibT_eX]

[DOI]

Céline Helbert

Victor Picheny

J. Glob. Optim., 2020

Multi-Output Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment.

[BibT_eX]

[DOI]

Déborah Idier

Jérémy Rohmer

CoRR, 2020

2019

Sensitivity indices for independent groups of variables.

[BibT_eX]

[DOI]

Math. Comput. Simul., 2019

Composite likelihood estimation for a Gaussian process under fixed domain asymptotics.

[BibT_eX]

[DOI]

Moreno Bevilacqua

Daira Velandia

J. Multivar. Anal., 2019

Gaussian Process-Based Dimension Reduction for Goal-Oriented Sequential Design.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2019

Rate of convergence for geometric inference based on the empirical Christoffel function.

[BibT_eX]

[DOI]

Mai Trang Vu

Edouard Pauwels

CoRR, 2019

Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC.

[BibT_eX]

[DOI]

Nicolas Durrande

CoRR, 2019

Learning a Gaussian Process Model on the Riemannian Manifold of Non-decreasing Distribution Functions.

[BibT_eX]

[DOI]

Proceedings of the PRICAI 2019: Trends in Artificial Intelligence, 2019

2018

A Gaussian Process Regression Model for Distribution Inputs.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2018

Nested Kriging predictions for datasets with a large number of observations.

[BibT_eX]

[DOI]

Stat. Comput., 2018

Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints.

[BibT_eX]

[DOI]

Nicolas Durrande

SIAM/ASA J. Uncertain. Quantification, 2018

Entropic Variable Boosting for Explainability and Interpretability in Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Cross-validation estimation of covariance parameters under fixed-domain asymptotics.

[BibT_eX]

[DOI]

Agnès Lagnoux

Thi Mong Ngoc Nguyen

J. Multivar. Anal., 2017

Optimal configurations of lines and a statistical application.

[BibT_eX]

[DOI]

Martin Ehler

Manuel Gräf

Adv. Comput. Math., 2017

2016

Asymptotic properties of multivariate tapering for estimation and prediction.

[BibT_eX]

[DOI]

Reinhard Furrer

Juan Du

J. Multivar. Anal., 2016

2014

Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2014

2013

Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification.

[BibT_eX]

[DOI]