76 citations · 106 across the 13 of their papers we have counts for
14 papers
Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization
Shuang Li, Zhihui Zhu, Qiuwei Li
We analyze Bregman ADMM for nonconvex linearly constrained problems under two-sided relative smoothness, a condition that replaces the standard Lipschitz gradient assumption with a…
Elastic fractal higher-order topological states
Tingfeng Ma, Bowei Wu, Jiachao Xu +5
Fractal is an intriguing geometry with self-similarity and non-integer dimensions, the elastic-wave topological phase based on fractal structures has not been revealed up to now. I…
Stochastic Natural Thresholding Algorithms
Rachel Grotheer, Shuang Li, Anna Ma +2
Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of…
SP2: A Second Order Stochastic Polyak Method
Shuang Li, William J. Swartworth, Martin Takáč +2
Recently the "SP" (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized…
Local and Global Convergence of General Burer-Monteiro Tensor Optimizations
Shuang Li, Qiuwei Li
Tensor optimization is crucial to massive machine learning and signal processing tasks. In this paper, we consider tensor optimization with a convex and well-conditioned objective…
The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery without Regularization
Shuang Li, Qiuwei Li, Zhihui Zhu +2
Low-rank matrix recovery is a fundamental problem in signal processing and machine learning. A recent very popular approach to recovering a low-rank matrix X is to factorize it as…
Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery
Rachel Grotheer, Shuang Li, Anna Ma +2
Low-rank tensor recovery problems have been widely studied in many applications of signal processing and machine learning. Tucker decomposition is known as one of the most popular…
Iterative Hard Thresholding for Low CP-rank Tensor Models
Rachel Grotheer, Shuang Li, Anna Ma +2
Recovery of low-rank matrices from a small number of linear measurements is now well-known to be possible under various model assumptions on the measurements. Such results demonstr…
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
Shuang Li, Gongguo Tang, Michael B. Wakin
The landscape of empirical risk has been widely studied in a series of machine learning problems, including low-rank matrix factorization, matrix sensing, matrix completion, and ph…
Atomic Norm Denoising for Complex Exponentials with Unknown Waveform Modulations
Shuang Li, Michael B. Wakin, Gongguo Tang
Non-stationary blind super-resolution is an extension of the traditional super-resolution problem, which deals with the problem of recovering fine details from coarse measurements.…
Recovery Analysis of Damped Spectrally Sparse Signals and Its Relation to MUSIC
Shuang Li, Hassan Mansour, Michael B. Wakin
One of the classical approaches for estimating the frequencies and damping factors in a spectrally sparse signal is the MUSIC algorithm, which exploits the low-rank structure of an…
Compressed Anomaly Detection with Multiple Mixed Observations
Natalie Durgin, Rachel Grotheer, Chenxi Huang +4
We consider a collection of independent random variables that are identically distributed, except for a small subset which follows a different, anomalous distribution. We study the…
Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors
Natalie Durgin, Rachel Grotheer, Chenxi Huang +4
While single measurement vector (SMV) models have been widely studied in signal processing, there is a surging interest in addressing the multiple measurement vectors (MMV) problem…
Atomic Norm Minimization for Modal Analysis from Random and Compressed Samples
Shuang Li, Dehui Yang, Gongguo Tang +1
Modal analysis is the process of estimating a system's modal parameters such as its natural frequencies and mode shapes. One application of modal analysis is in structural health m…