21 citations · 83 across the 10 of their papers we have counts for
11 papers
Large Graph Signal Denoising with Application to Differential Privacy
Elie Chedemail, Basile de Loynes, Fabien Navarro +1
Over the last decade, signal processing on graphs has become a very active area of research. Specifically, the number of applications, for instance in statistical or deep learning,…
Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors
Amay SM Cheam, Marc Fredette, Matthieu Marbac +1
The COVID-19 pandemic has taken the world by storm with its high infection rate. Investigating its geographical disparities has paramount interest in order to gauge its relationshi…
Gasper: GrAph Signal ProcEssing in R
Basile de Loynes, Fabien Navarro, Baptiste Olivier
We present a short tutorial on to the use of the R gasper package. Gasper is a package dedicated to signal processing on graphs. It also provides an interface to the SuiteSparse Ma…
Nonparametric estimation in a regression model with additive and multiplicative noise
Christophe Chesneau, Salima El Kolei, Junke Kou +1
In this paper, we consider an unknown functional estimation problem in a general nonparametric regression model with the feature of having both multiplicative and additive noise.We…
Localized Fourier Analysis for Graph Signal Processing
Basile de Loynes, Fabien Navarro, Baptiste Olivier
We propose a new point of view in the study of Fourier analysis on graphs, taking advantage of localization in the Fourier domain. For a signal on vertices of a weighted graph…
Data-driven Thresholding in Denoising with Spectral Graph Wavelet Transform
Basile de Loynes, Fabien Navarro, Baptiste Olivier
This paper is devoted to adaptive signal denoising in the context of Graph Signal Processing (GSP) using Spectral Graph Wavelet Transform (SGWT). This issue is addressed \emph{via}…
Mixture of hidden Markov models for accelerometer data
Marie du Roy de Chaumaray, Matthieu Marbac, Fabien Navarro
Motivated by the analysis of accelerometer data, we introduce a specific finite mixture of hidden Markov models with particular characteristics that adapt well to the specific natu…
Finite sample improvement of Akaike's Information Criterion
Adrien Saumard, Fabien Navarro
We emphasize that it is possible to improve the principle of unbiased risk estimation for model selection by addressing excess risk deviations in the design of penalization procedu…
Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases
Fabien Navarro, Adrien Saumard
We investigate the optimality for model selection of the so-called slope heuristics, -fold cross-validation and -fold penalization in a heteroscedastic with random design reg…
On adaptive wavelet estimation of a class of weighted densities
Fabien Navarro, Christophe Chesneau, Jalal Fadili
We investigate the estimation of a weighted density taking the form , where denotes an unknown density, the associated distribution function and is a known (no…
Block thresholding for wavelet-based estimation of function derivatives from a heteroscedastic multichannel convolution model
Fabien Navarro, Christophe Chesneau, Jalal Fadili +1
We observe heteroscedastic stochastic processes , where for any and , is the convolution product of an unknown functi…