In this work, we analyze the relation between reparametrizations of grad...
We consider covariance estimation of any subgaussian distribution from
f...
We propose a new algorithm for the problem of recovering data that adher...
As the array dimension of massive MIMO systems increases to unprecedente...
In this short note, we propose a new method for quantizing the weights o...
In many applications, solutions of numerical problems are required to be...
In deep learning it is common to overparameterize the neural networks, t...
In this self-contained chapter, we revisit a fundamental problem of
mult...
We consider the classical problem of estimating the covariance matrix of...
We consider the problem of recovering an unknown low-rank matrix X with
...
We propose a novel sparsity model for distributed compressed sensing in ...
We provide an explicit analysis of the dynamics of vanilla gradient desc...
This work is concerned with the problem of recovering high-dimensional
s...
We provide a comprehensive convergence study of the iterative multi-pena...
This paper studies the problem of recovering a signal from one-bit compr...
A simple hard-thresholding operation is shown to be able to recover L
si...