François Bachoc

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Featured Co-authors

Jean-Michel Loubes
47 publications
Marc G. Genton
42 publications
Gilles Blanchard
37 publications
Klaus Nordhausen
27 publications
David Ginsbourger
26 publications
Nicolas Durrande
23 publications
Vladimir Spokoiny
23 publications
Edouard Pauwels
20 publications
Reinhard Furrer
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Fabrice Gamboa
19 publications
Sébastien Gerchinovitz
18 publications

research

∙ 09/04/2023

Selective inference after convex clustering with ℓ_1 penalization

Classical inference methods notoriously fail when applied to data-driven...

0 François Bachoc, et al. ∙

research

∙ 08/28/2023

Improved learning theory for kernel distribution regression with two-stage sampling

The distribution regression problem encompasses many important statistic...

0 François Bachoc, et al. ∙

research

∙ 06/05/2023

A generalization of the CIRCE method for quantifying input model uncertainty in presence of several groups of experiments

The semi-empirical nature of best-estimate models closing the balance eq...

0 Guillaume Damblin, et al. ∙

research

∙ 11/30/2022

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

Some classical uncertainty quantification problems require the estimatio...

0 Julien Demange-Chryst, et al. ∙

research

∙ 10/12/2022

Gaussian Processes on Distributions based on Regularized Optimal Transport

We present a novel kernel over the space of probability measures based o...

0 François Bachoc, et al. ∙

research

∙ 09/21/2022

Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals

Non-stationary source separation is a well-established branch of blind s...

0 François Bachoc, et al. ∙

research

∙ 09/02/2022

Regret Analysis of Dyadic Search

We analyze the cumulative regret of the Dyadic Search algorithm of Bacho...

0 François Bachoc, et al. ∙

research

∙ 08/13/2022

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

This paper studies a natural generalization of the problem of minimizing...

0 François Bachoc, et al. ∙

research

∙ 06/15/2022

Local Identifiability of Deep ReLU Neural Networks: the Theory

Is a sample rich enough to determine, at least locally, the parameters o...

0 Joachim Bona-Pellissier, et al. ∙

research

∙ 05/17/2022

High-dimensional additive Gaussian processes under monotonicity constraints

We introduce an additive Gaussian process framework accounting for monot...

0 Andrés F. López-Lopera, et al. ∙

research

∙ 02/25/2022

Shapley effect estimation in reliability-oriented sensitivity analysis with correlated inputs by importance sampling

Reliability-oriented sensitivity analysis aims at combining both reliabi...

0 Julien Demange-Chryst, et al. ∙

research

∙ 02/22/2022

Multivariate Gaussian Random Fields over Generalized Product Spaces involving the Hypertorus

The paper deals with multivariate Gaussian random fields defined over ge...

0 François Bachoc, et al. ∙

research

∙ 12/24/2021

Parameter identifiability of a deep feedforward ReLU neural network

The possibility for one to recover the parameters-weights and biases-of ...

0 Joachim Bona-Pellissier, et al. ∙

research

∙ 12/14/2021

Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication

We provide posterior contraction rates for constrained deep Gaussian pro...

0 François Bachoc, et al. ∙

research

∙ 11/18/2021

Bounds in L^1 Wasserstein distance on the normal approximation of general M-estimators

We derive quantitative bounds on the rate of convergence in L^1 Wasserst...

0 François Bachoc, et al. ∙

research

∙ 07/05/2021

Blind source separation for non-stationary random fields

Regional data analysis is concerned with the analysis and modeling of me...

0 Christoph Muehlmann, et al. ∙

research

∙ 02/03/2021

The Sample Complexities of Global Lipschitz Optimization

We study the problem of black-box optimization of a Lipschitz function f...

0 François Bachoc, et al. ∙

research

∙ 11/03/2020

Test of the Latent Dimension of a Spatial Blind Source Separation Model

We assume a spatial blind source separation model in which the observed ...

0 Christoph Muehlmann, et al. ∙

research

∙ 10/26/2020

The sample complexity of level set approximation

We study the problem of approximating the level set of an unknown functi...

0 François Bachoc, et al. ∙

research

∙ 09/15/2020

Asymptotic analysis of maximum likelihood estimation of covariance parameters for Gaussian processes: an introduction with proofs

This article provides an introduction to the asymptotic analysis of cova...

0 François Bachoc, et al. ∙

research

∙ 09/09/2020

Sequential construction and dimension reduction of Gaussian processes under inequality constraints

Accounting for inequality constraints, such as boundedness, monotonicity...

0 François Bachoc, et al. ∙

research

∙ 07/29/2020

Asymptotically Equivalent Prediction in Multivariate Geostatistics

Cokriging is the common method of spatial interpolation (best linear unb...

0 François Bachoc, et al. ∙

research

∙ 06/03/2020

Gaussian linear approximation for the estimation of the Shapley effects

In this paper, we address the estimation of the sensitivity indices call...

0 Baptiste Broto, et al. ∙

research

∙ 04/08/2020

Sequential Design of Mixture Experiments with an Empirically Determined Input Domain and an Application to Burn-up Credit Penalization of Nuclear Fuel Rods

This paper presents methodologies for solving a common nuclear engineeri...

0 François Bachoc, et al. ∙

research

∙ 11/25/2019

Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes

The asymptotic analysis of covariance parameter estimation of Gaussian p...

0 François Bachoc, et al. ∙

research

∙ 10/31/2019

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

We consider the problem of estimating the support of a measure from a fi...

0 Mai Trang Vu, et al. ∙

research

∙ 07/30/2019

Block-diagonal covariance estimation and application to the Shapley effects in sensitivity analysis

In this paper, we aim to estimate block-diagonal covariance matrices for...

0 Baptiste Broto, et al. ∙

research

∙ 01/15/2019

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

Adding inequality constraints (e.g. boundedness, monotonicity, convexity...

0 Andrés F. López-Lopera, et al. ∙

research

∙ 12/21/2018

Spatial Blind Source Separation

Recently a blind source separation model was suggested for multivariate ...

0 François Bachoc, et al. ∙

research

∙ 12/21/2018

Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution

The Shapley effects are global sensitivity indices: they quantify the im...

0 Baptiste Broto, et al. ∙

research

∙ 10/18/2018

Entropic Variable Boosting for Explainability and Interpretability in Machine Learning

In this paper, we present a new explainability formalism to make clear t...

0 François Bachoc, et al. ∙

research

∙ 07/24/2018

Composite likelihood estimation for a Gaussian process under fixed domain asymptotics

We study composite likelihood estimation of the covariance parameters wi...

0 François Bachoc, et al. ∙

research

∙ 06/08/2018

Semi-parametric estimation of the variogram of a Gaussian process with stationary increments

We consider the semi-parametric estimation of a scale parameter of a one...

0 Jean-Marc Azaïs, et al. ∙

research

∙ 05/02/2018

Gaussian Process Forecast with multidimensional distributional entries

In this work, we propose to define Gaussian Processes indexed by multidi...

0 François Bachoc, et al. ∙

research

∙ 04/20/2018

On the Post Selection Inference constant under Restricted Isometry Properties

Uniformly valid confidence intervals post model selection in regression ...

0 François Bachoc, et al. ∙

research

∙ 04/10/2018

Maximum likelihood estimation for Gaussian processes under inequality constraints

We consider covariance parameter estimation for a Gaussian process under...

0 François Bachoc, et al. ∙

research

∙ 03/16/2018

Gaussian Processes indexed on the symmetric group: prediction and learning

In the framework of the supervised learning of a real function defined o...

0 François Bachoc, et al. ∙

research

∙ 01/12/2018

Sensitivity indices for independent groups of variables

In this paper, we study sensitivity indices in an additive model and for...

0 Baptiste Broto, et al. ∙

research

∙ 10/20/2017

Finite-dimensional Gaussian approximation with linear inequality constraints

Introducing inequality constraints in Gaussian process (GP) models can l...

0 Andrés F. López-Lopera, et al. ∙

research

∙ 01/31/2017

Gaussian Process Regression Model for Distribution Inputs

Monge-Kantorovich distances, otherwise known as Wasserstein distances, h...

0 François Bachoc, et al. ∙

research

∙ 08/03/2016

A supermartingale approach to Gaussian process based sequential design of experiments

Gaussian process (GP) models have become a well-established frameworkfor...

0 Julien Bect, et al. ∙

research

∙ 07/19/2016

Nested Kriging predictions for datasets with large number of observations

This work falls within the context of predicting the value of a real fun...

0 Didier Rullière, et al. ∙

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