Classical inference methods notoriously fail when applied to data-driven...
The distribution regression problem encompasses many important statistic...
The semi-empirical nature of best-estimate models closing the balance
eq...
Some classical uncertainty quantification problems require the estimatio...
We present a novel kernel over the space of probability measures based o...
Non-stationary source separation is a well-established branch of blind s...
We analyze the cumulative regret of the Dyadic Search algorithm of Bacho...
This paper studies a natural generalization of the problem of minimizing...
Is a sample rich enough to determine, at least locally, the parameters o...
We introduce an additive Gaussian process framework accounting for
monot...
Reliability-oriented sensitivity analysis aims at combining both reliabi...
The paper deals with multivariate Gaussian random fields defined over
ge...
The possibility for one to recover the parameters-weights and biases-of ...
We provide posterior contraction rates for constrained deep Gaussian
pro...
We derive quantitative bounds on the rate of convergence in L^1 Wasserst...
Regional data analysis is concerned with the analysis and modeling of
me...
We study the problem of black-box optimization of a Lipschitz function f...
We assume a spatial blind source separation model in which the observed
...
We study the problem of approximating the level set of an unknown functi...
This article provides an introduction to the asymptotic analysis of
cova...
Accounting for inequality constraints, such as boundedness, monotonicity...
Cokriging is the common method of spatial interpolation (best linear unb...
In this paper, we address the estimation of the sensitivity indices call...
This paper presents methodologies for solving a common nuclear engineeri...
The asymptotic analysis of covariance parameter estimation of Gaussian
p...
We consider the problem of estimating the support of a measure from a fi...
In this paper, we aim to estimate block-diagonal covariance matrices for...
Adding inequality constraints (e.g. boundedness, monotonicity, convexity...
Recently a blind source separation model was suggested for multivariate
...
The Shapley effects are global sensitivity indices: they quantify the im...
In this paper, we present a new explainability formalism to make clear t...
We study composite likelihood estimation of the covariance parameters wi...
We consider the semi-parametric estimation of a scale parameter of a
one...
In this work, we propose to define Gaussian Processes indexed by
multidi...
Uniformly valid confidence intervals post model selection in regression ...
We consider covariance parameter estimation for a Gaussian process under...
In the framework of the supervised learning of a real function defined o...
In this paper, we study sensitivity indices in an additive model and for...
Introducing inequality constraints in Gaussian process (GP) models can l...
Monge-Kantorovich distances, otherwise known as Wasserstein distances, h...
Gaussian process (GP) models have become a well-established frameworkfor...
This work falls within the context of predicting the value of a real fun...