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09/04/2023
Selective inference after convex clustering with ℓ_1 penalization
Classical inference methods notoriously fail when applied to data-driven...
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08/28/2023
Improved learning theory for kernel distribution regression with two-stage sampling
The distribution regression problem encompasses many important statistic...
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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...
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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...
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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...
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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...
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09/02/2022
Regret Analysis of Dyadic Search
We analyze the cumulative regret of the Dyadic Search algorithm of Bacho...
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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...
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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...
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05/17/2022
High-dimensional additive Gaussian processes under monotonicity constraints
We introduce an additive Gaussian process framework accounting for monot...
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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...
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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...
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12/24/2021
Parameter identifiability of a deep feedforward ReLU neural network
The possibility for one to recover the parameters-weights and biases-of ...
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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...
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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...
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07/05/2021
Blind source separation for non-stationary random fields
Regional data analysis is concerned with the analysis and modeling of me...
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02/03/2021
The Sample Complexities of Global Lipschitz Optimization
We study the problem of black-box optimization of a Lipschitz function f...
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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 ...
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10/26/2020
The sample complexity of level set approximation
We study the problem of approximating the level set of an unknown functi...
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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...
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09/09/2020
Sequential construction and dimension reduction of Gaussian processes under inequality constraints
Accounting for inequality constraints, such as boundedness, monotonicity...
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07/29/2020
Asymptotically Equivalent Prediction in Multivariate Geostatistics
Cokriging is the common method of spatial interpolation (best linear unb...
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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...
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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...
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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...
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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...
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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...
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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...
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12/21/2018
Spatial Blind Source Separation
Recently a blind source separation model was suggested for multivariate ...
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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...
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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...
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07/24/2018
Composite likelihood estimation for a Gaussian process under fixed domain asymptotics
We study composite likelihood estimation of the covariance parameters wi...
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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...
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05/02/2018
Gaussian Process Forecast with multidimensional distributional entries
In this work, we propose to define Gaussian Processes indexed by multidi...
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04/20/2018
On the Post Selection Inference constant under Restricted Isometry Properties
Uniformly valid confidence intervals post model selection in regression ...
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04/10/2018
Maximum likelihood estimation for Gaussian processes under inequality constraints
We consider covariance parameter estimation for a Gaussian process under...
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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...
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01/12/2018
Sensitivity indices for independent groups of variables
In this paper, we study sensitivity indices in an additive model and for...
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10/20/2017
Finite-dimensional Gaussian approximation with linear inequality constraints
Introducing inequality constraints in Gaussian process (GP) models can l...
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01/31/2017
Gaussian Process Regression Model for Distribution Inputs
Monge-Kantorovich distances, otherwise known as Wasserstein distances, h...
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08/03/2016
A supermartingale approach to Gaussian process based sequential design of experiments
Gaussian process (GP) models have become a well-established frameworkfor...
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07/19/2016
