K-means clustering is a widely used machine learning method for identify...
We study the problem of estimating a low-rank matrix from noisy measurem...
To rigorously certify the robustness of neural networks to adversarial
p...
This paper focuses on complete dictionary learning problem, where the go...
The matrix completion problem seeks to recover a d× d ground truth
matri...
We consider minimizing a twice-differentiable, L-smooth, and μ-strongly
...
We consider using gradient descent to minimize the nonconvex function
f(...
We prove that it is possible for nonconvex low-rank matrix recovery to
c...
The robustness of a neural network to adversarial examples can be provab...
Nonconvex matrix recovery is known to contain no spurious local minima u...
When the linear measurements of an instance of low-rank matrix recovery
...
The sparse inverse covariance estimation problem is commonly solved usin...
The sparse inverse covariance estimation problem is commonly solved usin...
In this paper, we consider the Graphical Lasso (GL), a popular optimizat...