Substantial progress has been made recently on develo** provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …

For many modern applications in science and engineering, data are collected in a streaming
fashion carrying time-varying information, and practitioners need to process them with a …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

As a paradigm to recover unknown entries of a matrix from partial observations, low-rank
matrix completion (LRMC) has generated a great deal of interest. Over the years, there have …

Abstract We propose $\textsf {ScaledGD ($\lambda $)} $, a preconditioned gradient descent
method to tackle the low-rank matrix sensing problem when the true rank is unknown, and …

Deep neural networks are vulnerable to adversarial attacks. The literature is rich with
algorithms that can easily craft successful adversarial examples. In contrast, the …

Low-rank matrix estimation is a canonical problem that finds numerous applications in signal
processing, machine learning and imaging science. A popular approach in practice is to …

In the past decade, sparse and low-rank recovery has drawn much attention in many areas
such as signal/image processing, statistics, bioinformatics, and machine learning. To …

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 …

Motivated by recent increased interest in optimization algorithms for non-convex
optimization in application to training deep neural networks and other optimization problems …