Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Modern neural networks are typically trained in an over-parameterized regime where the
parameters of the model far exceed the size of the training data. Such neural networks in …

In the last decade, there has been considerable interest in understanding when it is possible
to find structured solutions to underdetermined systems of linear equations. This paper …

Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for
multilinear data analysis against outliers, gross corruptions, and missing values and has a …

In recent years, low-rank based tensor completion, which is a higher order extension of
matrix completion, has received considerable attention. However, the low-rank assumption …

At the core of many sensing and imaging applications, the signal of interest can be modeled
as a linear superposition of translated or modulated versions of some template [eg, a point …

The paper studies the problem of recovering a spectrally sparse object from a small number
of time domain samples. Specifically, the object of interest with ambient dimension n is …

In this article, we study robust tensor completion by using transformed tensor singular value
decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier …

Suppose we have samples of a subset of a collection of random variables. No additional
information is provided about the number of latent variables, nor of the relationship between …

Over seventy years ago, Richard Bellman coined the term “the curse of dimensionality” to
describe phenomena and computational challenges that arise in high dimensions. These …