Shuyang Ling
Shuyang Ling
Assistant Professor, New York University Shanghai
Verified email at - Homepage
Cited by
Cited by
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
Self-calibration and biconvex compressive sensing
S Ling, T Strohmer
Inverse Problems 31 (11), 115002, 2015
Blind deconvolution meets blind demixing: algorithms and performance bounds
S Ling, T Strohmer
IEEE Transactions on Information Theory 63 (7), 4497-4520, 2017
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
Self-calibration and bilinear inverse problems via linear least squares
S Ling, T Strohmer
SIAM Journal on Imaging Sciences 11 (1), 252-292, 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
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
Near-optimal performance bounds for orthogonal and permutation group synchronization via spectral methods
S Ling
Applied and Computational Harmonic Analysis 60, 20-52, 2022
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
Strong consistency, graph Laplacians, and the stochastic block model
S Deng, S Ling, T Strohmer
The Journal of Machine Learning Research 22 (117), 1-44, 2021
Solving orthogonal group synchronization via convex and low-rank optimization: tightness and landscape analysis
S Ling
Mathematical Programming, Series A 200, 589–628, 2023
Improved performance guarantees for orthogonal group synchronization via generalized power method
S Ling
SIAM Journal on Optimization 32 (2), 1018-1048, 2022
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
Generalized orthogonal Procrustes problem under arbitrary adversaries
S Ling
arXiv preprint arXiv:2106.15493, 2024
Near-optimal bounds for generalized orthogonal Procrustes problem via generalized power method
S Ling
Applied and Computational Harmonic Analysis 66, 62-100, 2023
Neural collapse for unconstrained feature model under cross-entropy loss with imbalanced data
W Hong, S Ling
The Journal of Machine Learning Research 25, 1-48, 2024
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
Local geometry determines global landscape in low-rank factorization for synchronization
S Ling
arXiv preprint arXiv 2311.18670, 2023
On the critical coupling of the finite Kuramoto model on dense networks
S Ling
arXiv preprint arXiv:2004.03202, 2020
Fast blind deconvolution and blind demixing via nonconvex optimization
S Ling, T Strohmer
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
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