Tensor train decomposition is widely used in machine learning and quantu...
Spectral methods include a family of algorithms related to the eigenvect...
The problem of estimating a sparse signal from low dimensional noisy
obs...
The landscape of empirical risk has been widely studied in a series of
m...
The (global) Lipschitz smoothness condition is crucial in establishing t...
Principal Component Analysis (PCA) is one of the most important methods ...
Non-stationary blind super-resolution is an extension of the traditional...
The task of finding a sparse signal decomposition in an overcomplete
dic...
We study the convergence of a variant of distributed gradient descent (D...
We study the ubiquitous problem of super-resolution in which one aims at...
This work investigates the geometry of a nonconvex reformulation of
mini...
Fourier ptychography is a new computational microscopy technique that
pr...
Tensors play a central role in many modern machine learning and signal
p...
This paper studies the sample complexity of searching over multiple
popu...