Shuyang Ling
Shuyang Ling
New York University Shanghai
Verified email at nyu.edu - Homepage
Title
Cited by
Cited by
Year
Self-calibration and biconvex compressive sensing
S Ling, T Strohmer
Inverse Problems 31 (11), 115002, 2015
1712015
Rapid, robust, and reliable blind deconvolution via nonconvex optimization
X Li, S Ling, T Strohmer, K Wei
Applied and Computational Harmonic Analysis 47 (3), 893-934, 2019
1592019
Blind deconvolution meets blind demixing: algorithms and performance bounds
S Ling, T Strohmer
IEEE Transactions on Information Theory 63 (7), 4497-4520, 2017
862017
Self-calibration and bilinear inverse problems via linear least squares
S Ling, T Strohmer
SIAM Journal on Imaging Sciences 11 (1), 252-292, 2018
39*2018
Regularized gradient descent: a nonconvex recipe for fast joint blind deconvolution and demixing
S Ling, T Strohmer
Information and Inference: A Journal of the IMA 8 (1), 1-49, 2019
342019
When do birds of a feather flock together? k-means, proximity, and conic programming
X Li, Y Li, S Ling, T Strohmer, K Wei
Mathematical Programming, Series A 179 (1), 295-341, 2020
202020
Backward error and perturbation bounds for high order Sylvester tensor equation
X Shi, Y Wei, S Ling
Linear and Multilinear Algebra 61 (10), 1436-1446, 2013
202013
On the landscape of synchronization networks: a perspective from nonconvex optimization
S Ling, R Xu, AS Bandeira
SIAM Journal on Optimization 29 (3), 1879-1907, 2019
142019
Certifying global optimality of graph cuts via semidefinite relaxation: a performance guarantee for spectral clustering
S Ling, T Strohmer
Foundations of Computational Mathematics 20 (3), 368-421, 2020
92020
Solving orthogonal group synchronization via convex and low-rank optimization: tightness and landscape analysis
S Ling
arXiv preprint arXiv:2006.00902, 2020
22020
Strong consistency, graph Laplacians, and the stochastic block model
S Deng, S Ling, T Strohmer
arXiv preprint arXiv:2004.09780, 2020
22020
Near-optimal performance bounds for orthogonal and permutation group synchronization via spectral methods
S Ling
arXiv preprint arXiv:2008.05341, 2020
12020
Fast blind deconvolution and blind demixing via nonconvex optimization
S Ling, T Strohmer
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
12017
Simultaneous blind deconvolution and blind demixing via convex programming
S Ling, T Strohmer
2016 50th Asilomar Conference on Signals, Systems and Computers, 1223-1227, 2016
12016
On the critical coupling of the finite Kuramoto model on dense networks
S Ling
arXiv preprint arXiv:2004.03202, 2020
2020
DS-GA 3001 Special Topics in Data Science: Mathematics of Data Science: Graphs and Networks (Spring 2018)
AS Bandeira, S Ling
2018
You can have it all -- Fast algorithms for blind deconvolution, self-calibration, and demixing
S Ling, T Strohmer
Mathematics in Imaging, MW1C.1, 2017
2017
Bilinear inverse problems: theory, algorithms, and applications
S Ling
University of California, Davis, 2017
2017
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