2 citations · 2 across the 1 of their papers we have counts for
6 papers
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples
Richard Y. Zhang
The robustness of a neural network to adversarial examples can be provably certified by solving a convex relaxation. If the relaxation is loose, however, then the resulting certifi…
Large-Scale Traffic Signal Offset Optimization
Yi Ouyang, Richard Y. Zhang, Javad Lavaei +1
The offset optimization problem seeks to coordinate and synchronize the timing of traffic signals throughout a network in order to enhance traffic flow and reduce stops and delays.…
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery
Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei
Nonconvex matrix recovery is known to contain no spurious local minima under a restricted isometry property (RIP) with a sufficiently small RIP constant . If is too large, h…
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
Richard Y. Zhang, Cédric Josz, Somayeh Sojoudi +1
When the linear measurements of an instance of low-rank matrix recovery satisfy a restricted isometry property (RIP)---i.e. they are approximately norm-preserving---the problem is…
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Richard Y. Zhang, Salar Fattahi, Somayeh Sojoudi
The sparse inverse covariance estimation problem is commonly solved using an -regularized Gaussian maximum likelihood estimator known as "graphical lasso", but its comput…
Sparse Inverse Covariance Estimation for Chordal Structures
Salar Fattahi, Richard Y. Zhang, Somayeh Sojoudi
In this paper, we consider the Graphical Lasso (GL), a popular optimization problem for learning the sparse representations of high-dimensional datasets, which is well-known to be…