We analyze the performance of the least absolute shrinkage and selection...
In realistic compressed sensing (CS) scenarios, the obtained measurement...
We consider the ubiquitous linear inverse problems with additive Gaussia...
We consider the general problem of recovering a high-dimensional signal ...
We study the inference problem in the group testing to identify defectiv...
We consider the problem of high-dimensional Ising model selection using
...
We develop a message-passing algorithm for noisy matrix completion probl...
We theoretically investigate the performance of ℓ_1-regularized linear
r...
Inferring interaction parameters from observed data is a ubiquitous
requ...
We propose a Monte-Carlo-based method for reconstructing sparse signals ...
We consider the variable selection problem of generalized linear models
...
Vector approximate message passing (VAMP) is an efficient approximate
in...
We investigate the learning performance of the pseudolikelihood maximiza...
In order to solve large matrix completion problems with practical
comput...
Resampling techniques are widely used in statistical inference and ensem...
Learning the undirected graph structure of a Markov network from data is...
An algorithmic limit of compressed sensing or related variable-selection...
In high-dimensional statistical inference in which the number of paramet...
An approximate method for conducting resampling in Lasso, the ℓ_1
penali...
We develop an approximate formula for evaluating a cross-validation esti...
Cross-validation (CV) is a technique for evaluating the ability of
stati...
We analyse the matrix factorization problem. Given a noisy measurement o...