11 citations · 25 across the 14 of their papers we have counts for
15 papers
Sparse additive models in high dimensions with wavelets
Sylvain Sardy, Xiaoyu Ma
In multivariate regression, when covariates are numerous, it is often reasonable to assume that only a small number of them has predictive information. In some medical applications…
A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks
Xiaoyu Ma, Sylvain Sardy, Nick Hengartner +2
To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability…
What needles do sparse neural networks find in nonlinear haystacks
Sylvain Sardy, Nicolas W Hengartner, Nikolai Bonenko +1
Using a sparsity inducing penalty in artificial neural networks (ANNs) avoids over-fitting, especially in situations where noise is high and the training set is small in comparison…
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
Pascaline Descloux, Claire Boyer, Julie Josse +2
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology, initially introduced for sparse linear models, to the sparse corruptions problem. We give theoretical guar…
Model selection with lasso-zero: adding straw to the haystack to better find needles
Pascaline Descloux, Sylvain Sardy
The high-dimensional linear model is considered and the focus is put on the problem of recovering the support of the sparse vector We introduce Lasso-Z…
Thresholding tests
Sylvain Sardy, Caroline Giacobino, Jairo Diaz-Rodriguez
We derive a new class of statistical tests for generalized linear models based on thresholding point estimators. These tests can be employed whether the model includes more paramet…
Nonparametric estimation of galaxy cluster's emissivity and point source detection in astrophysics with two lasso penalties
Jairo Diaz-Rodriguez, Dominique Eckert, Hatef Monajemi +2
Astrophysicists are interested in recovering the 3D gas emissivity of a galaxy cluster from a 2D image taken by a telescope. A blurring phenomenon and presence of point sources mak…
Low-rank model with covariates for count data analysis
Geneviève Robin, Julie Josse, Eric Moulines +1
Count data are collected in many scientific and engineering tasks including image processing, single-cell RNA sequencing and ecological studies. Such data sets often contain missin…
Threshold Selection for Total Variation Denoising
Sylvain Sardy, Hatef Monajemi
Total variation (TV) denoising is a nonparametric smoothing method that has good properties for preserving sharp edges and contours in objects with spatial structures like natural…
denoiseR: A Package for Low Rank Matrix Estimation
Julie Josse, Sylvain Sardy, Stefan Wager
We introduce denoiseR, an R package that provides a unified implementation of several state-of-the-art proposals for regularized low rank matrix estimation, along with automatic se…
Quantile universal threshold for model selection
Caroline Giacobino, Sylvain Sardy, Jairo Diaz-Rodriguez +1
Efficient recovery of a low-dimensional structure from high-dimensional data has been pursued in various settings including wavelet denoising, generalized linear models and low-ran…
Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression
Jairo Diaz-Rodriguez, Sylvain Sardy
To estimate a sparse linear model from data with Gaussian noise, consilience from lasso and compressed sensing literatures is that thresholding estimators like lasso and the Dantzi…
Adaptive Shrinkage of singular values
Julie Josse, Sylvain Sardy
To recover a low rank structure from a noisy matrix, truncated singular value decomposition has been extensively used and studied. Recent studies suggested that the signal can be b…
Blockwise and coordinatewise thresholding to combine tests of different natures in modern ANOVA
Sylvain Sardy
We derive new tests for fixed and random ANOVA based on a thresholded point estimate. The pivotal quantity is the threshold that sets all the coefficients of the null hypothesis to…
Smooth blockwise iterative thresholding: a smooth fixed point estimator based on the likelihood's block gradient
Sylvain Sardy
The proposed smooth blockwise iterative thresholding estimator (SBITE) is a model selection technique defined as a fixed point reached by iterating a likelihood gradient-based thre…