| 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 | 248 | 2019 |
| Self-calibration and biconvex compressive sensing S Ling, T Strohmer Inverse Problems 31 (11), 115002, 2015 | 234 | 2015 |
| Blind deconvolution meets blind demixing: algorithms and performance bounds S Ling, T Strohmer IEEE Transactions on Information Theory 63 (7), 4497-4520, 2017 | 124 | 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 | 83 | 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 | 61* | 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 | 60 | 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 | 54 | 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 | 50 | 2022 |
| 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 | 41 | 2021 |
| 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 | 34 | 2013 |
| Solving orthogonal group synchronization via convex and low-rank optimization: tightness and landscape analysis S Ling Mathematical Programming, Series A 200, 589–628, 2023 | 28 | 2023 |
| Improved performance guarantees for orthogonal group synchronization via generalized power method S Ling SIAM Journal on Optimization 32 (2), 1018-1048, 2022 | 25 | 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 | 22 | 2020 |
| Near-optimal bounds for generalized orthogonal Procrustes problem via generalized power method S Ling Applied and Computational Harmonic Analysis 66, 62-100, 2023 | 13 | 2023 |
| Generalized orthogonal Procrustes problem under arbitrary adversaries S Ling SIAM Journal on Matrix Analysis and Applications 46 (1), 561--583, 2025 | 12* | 2025 |
| Neural collapse for unconstrained feature model under cross-entropy loss with imbalanced data W Hong, S Ling The Journal of Machine Learning Research 25 (192), 1-48, 2024 | 12 | 2024 |
| Cross entropy versus label smoothing: a neural collapse perspective L Guo, K Ross, Z Zhao, A George, S Ling, Y Xu, Z Dong arXiv preprint arXiv:2402.03979, 2024 | 5 | 2024 |
| Beyond unconstrained features: neural collapse for shallow neural networks with general data W Hong, S Ling arXiv preprint arXiv:2409.01832, 2024 | 3 | 2024 |
| On the exactness of SDP relaxation for quadratic assignment problem S Ling arXiv preprint arXiv:2408.05942, 2024 | 3 | 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 | 3 | 2016 |
Xiaodong LiAssociate Professor, University of California at DavisVerified email at ucdavis.edu
