activity
20162020
most citedStrong Consistency, Graph Laplacians, and the Stochastic Block Model

4 citations · 8 across the 3 of their papers we have counts for

collaborators

6 papers

math.OC20203 cited

Solving Orthogonal Group Synchronization via Convex and Low-Rank Optimization: Tightness and Landscape Analysis

Shuyang Ling

Group synchronization aims to recover the group elements from their noisy pairwise measurements. It has found many applications in community detection, clock synchronization, and j…

stat.ML20204 cited

Strong Consistency, Graph Laplacians, and the Stochastic Block Model

Shaofeng Deng, Shuyang Ling, Thomas Strohmer

Spectral clustering has become one of the most popular algorithms in data clustering and community detection. We study the performance of classical two-step spectral clustering via…

nlin.AO20201 cited

On the Critical Coupling of the Finite Kuramoto Model on Dense Networks

Shuyang Ling

Kuramoto model is one of the most prominent models for the synchronization of coupled oscillators. It has long been a research hotspot to understand how natural frequencies, the in…

math.OC2018

On the Landscape of Synchronization Networks: A Perspective from Nonconvex Optimization

Shuyang Ling, Ruitu Xu, Afonso S. Bandeira

Studying the landscape of nonconvex cost function is key towards a better understanding of optimization algorithms widely used in signal processing, statistics, and machine learnin…

stat.ML2018

Certifying Global Optimality of Graph Cuts via Semidefinite Relaxation: A Performance Guarantee for Spectral Clustering

Shuyang Ling, Thomas Strohmer

Spectral clustering has become one of the most widely used clustering techniques when the structure of the individual clusters is non-convex or highly anisotropic. Yet, despite its…

cs.IT2016

Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization

Xiaodong Li, Shuyang Ling, Thomas Strohmer +1

We study the question of reconstructing two signals and from their convolution . This problem, known as {\em blind deconvolution}, pervades many areas of scien…