16 citations · 69 across the 27 of their papers we have counts for
20 papers · 1 filter
Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization
Romane Giard, Yohann de Castro, Clément Marteau
This paper addresses the statistical estimation of Gaussian Mixture Models (GMMs) with unknown diagonal covariances from independent and identically distributed samples. We employ…
Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test
Jean-Marc Azaïs, Federico Dalmao, Yohann De Castro
In this article, we introduce the novel concept of the second maximum of a Gaussian random field defined on a Riemannian manifold: Once the field reaches its global maximum at a po…
SIGLE: a valid procedure for Selective Inference with the Generalized Linear Lasso
Quentin Duchemin, Yohann de Castro
This article investigates uncertainty quantification of the generalized linear lasso~(GLL), a popular variable selection method in high-dimensional regression settings. In many fie…
Three rates of convergence or separation via U-statistics in a dependent framework
Quentin Duchemin, Yohann De Castro, Claire Lacour
Despite the ubiquity of U-statistics in modern Probability and Statistics, their non-asymptotic analysis in a dependent framework may have been overlooked. In a recent work, a new…
Multiple Testing and Variable Selection along the path of the Least Angle Regression
J. -M. Azaïs, Y. De Castro
We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algorithm in high dimensions under the assumption of Gaussian noise. LARS is known to…
Adaptive Estimation of Nonparametric Geometric Graphs
Yohann De Castro, Claire Lacour, Thanh Mai Pham Ngoc
This article studies the recovery of graphons when they are convolution kernels on compact (symmetric) metric spaces. This case is of particular interest since it covers the situat…
Approximate Optimal Designs for Multivariate Polynomial Regression
Yohann De Castro, Fabrice Gamboa, Didier Henrion +2
We introduce a new approach aiming at computing approximate optimal designs for multivariate polynomial regressions on compact (semi-algebraic) design spaces. We use the moment-sum…
Testing Gaussian Process with Applications to Super-Resolution
Jean-Marc Azaïs, Yohann De Castro, Stéphane Mourareau
This article introduces exact testing procedures on the mean of a Gaussian process derived from the outcomes of -minimization over the space of complex valued measures.…
Sparse Recovery from Extreme Eigenvalues Deviation Inequalities
Sandrine Dallaporta, Yohann De Castro
This article provides a new toolbox to derive sparse recovery guarantees from small deviations on extreme singular values or extreme eigenvalues obtained in Random Matrix Theory. T…
Reconstructing undirected graphs from eigenspaces
Yohann De Castro, Thibault Espinasse, Paul Rochet
In this paper, we aim at recovering an undirected weighted graph of vertices from the knowledge of a perturbed version of the eigenspaces of its adjacency matrix . For insta…
Consistent estimation of the filtering and marginal smoothing distributions in nonparametric hidden Markov models
Yohann De Castro, Elisabeth Gassiat, Sylvain Le Corff
In this paper, we consider the filtering and smoothing recursions in nonparametric finite state space hidden Markov models (HMMs) when the parameters of the model are unknown and r…
Power of the Spacing test for Least-Angle Regression
Jean-Marc Azaïs, Yohann de Castro, Stéphane Mourareau
Recent advances in Post-Selection Inference have shown that conditional testing is relevant and tractable in high-dimensions. In the Gaussian linear model, further works have deriv…
Minimax adaptive estimation of nonparametric hidden Markov models
Yohann De Castro, Élisabeth Gassiat, Claire Lacour
We consider stationary hidden Markov models with finite state space and nonparametric modeling of the emission distributions. It has remained unknown until very recently that such…
Estimating the transition matrix of a Markov chain observed at random times
Flavia Barsotti, Yohann De Castro, Thibault Espinasse +1
In this paper we develop a statistical estimation technique to recover the transition kernel of a Markov chain in presence of censored data. We cons…
A Rice method proof of the Null-Space Property over the Grassmannian
J. -M Azaïs, Y. De Castro, S. Mourareau
The Null-Space Property (NSP) is a necessary and sufficient condition for the recovery of the largest coefficients of solutions to an under-determined system of linear equations. I…
Non-uniform spline recovery from small degree polynomial approximation
Yohann De Castro, Guillaume Mijoule
We investigate the sparse spikes deconvolution problem onto spaces of algebraic polynomials. Our framework encompasses the measure reconstruction problem from a combination of nois…
Spike detection from inaccurate samplings
Jean-Marc Azais, Yohann De Castro, Fabrice Gamboa
This article investigates the support detection problem using the LASSO estimator in the space of measures. More precisely, we study the recovery of a discrete measure (spike train…
A Remark on the Lasso and the Dantzig Selector
Yohann de Castro
This article investigates a new parameter for the high-dimensional regression with noise: the distortion. This latter has attracted a lot of attention recently with the appearance…
Exact Reconstruction using Beurling Minimal Extrapolation
Yohann de Castro, Fabrice Gamboa
We show that measures with finite support on the real line are the unique solution to an algorithm, named generalized minimal extrapolation, involving only a finite number of gener…
Optimal designs for Lasso and Dantzig selector using Expander Codes
Yohann de Castro
We investigate the high-dimensional regression problem using adjacency matrices of unbalanced expander graphs. In this frame, we prove that the -prediction error and the…