Graph Neural Networks (GNNs) have emerged as a powerful tool for data-dr...
This paper studies well-posedness and parameter sensitivity of the Squar...
We propose a class of greedy algorithms for weighted sparse recovery by
...
The past decade has seen increasing interest in applying Deep Learning (...
This paper concerns the approximation of smooth, high-dimensional functi...
In Bora et al. (2017), a mathematical framework was developed for compre...
This paper provides a variational analysis of the unconstrained formulat...
Sparse polynomial approximation has become indispensable for approximati...
Motivated by the question of optimal functional approximation via compre...
The accurate approximation of scalar-valued functions from sample points...
The sparsity in levels model recently inspired a new generation of effec...
We study sparse recovery with structured random measurement matrices hav...
Often in language and other areas of cognition, whether two components o...
This work is motivated by the difficulty in assembling the Galerkin matr...
A signature result in compressed sensing is that Gaussian random samplin...
We show the potential of greedy recovery strategies for the sparse
appro...
We present improved sampling complexity bounds for stable and robust spa...