| Nonconvex robust low-rank matrix recovery X Li, Z Zhu, A Man-Cho So, R Vidal SIAM Journal on Optimization 30 (1), 660-686, 2020 | 112 | 2020 |
| Weakly convex optimization over Stiefel manifold using Riemannian subgradient-type methods X Li, S Chen, Z Deng, Q Qu, Z Zhu, A Man-Cho So SIAM Journal on Optimization 31 (3), 1605-1634, 2021 | 89* | 2021 |
| Geometric analysis of nonconvex optimization landscapes for overcomplete learning Q Qu, Y Zhai, X Li, Y Zhang, Z Zhu International Conference on Learning Representations, 2020 | 48* | 2020 |
| Dropping symmetry for fast symmetric nonnegative matrix factorization Z Zhu, X Li, K Liu, Q Li Advances in Neural Information Processing Systems 31, 2018 | 48 | 2018 |
| Incremental methods for weakly convex optimization X Li, Z Zhu, AMC So, JD Lee arXiv preprint arXiv:1907.11687, 2019 | 47 | 2019 |
| Convergence of random reshuffling under the Kurdyka-Lojasiewicz inequality X Li, A Milzarek, J Qiu SIAM Journal on Optimization 33 (2), 1092-1120, 2023 | 41 | 2023 |
| A nonconvex approach for exact and efficient multichannel sparse blind deconvolution Q Qu, X Li, Z Zhu Advances in neural information processing systems 32, 2019 | 37 | 2019 |
| Designing robust sensing matrix for image compression G Li, X Li, S Li, H Bai, Q Jiang, X He IEEE Transactions on Image Processing 24 (12), 5389-5400, 2015 | 37 | 2015 |
| Optimized structured sparse sensing matrices for compressive sensing T Hong, X Li, Z Zhu, Q Li Signal processing 159, 119-129, 2019 | 30 | 2019 |
| Finding the sparsest vectors in a subspace: Theory, algorithms, and applications Q Qu, Z Zhu, X Li, MC Tsakiris, J Wright, R Vidal arXiv preprint arXiv:2001.06970, 2020 | 22 | 2020 |
| Distributed random reshuffling over networks K Huang, X Li, A Milzarek, S Pu, J Qiu IEEE Transactions on Signal Processing 71, 1143-1158, 2023 | 15 | 2023 |
| A provable splitting approach for symmetric nonnegative matrix factorization X Li, Z Zhu, Q Li, K Liu IEEE Transactions on Knowledge and Data Engineering 35 (3), 2206-2219, 2021 | 14 | 2021 |
| A unified convergence theorem for stochastic optimization methods X Li, A Milzarek Advances in Neural Information Processing Systems 35, 33107-33119, 2022 | 13 | 2022 |
| Convergence analysis of alternating nonconvex projections Z Zhu, X Li arXiv preprint arXiv:1802.03889, 2018 | 10 | 2018 |
| BAdam: A memory efficient full parameter optimization method for large language models Q Luo, H Yu, X Li The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 9* | 2024 |
| Exact recovery of multichannel sparse blind deconvolution via gradient descent Q Qu, X Li, Z Zhu SIAM Journal on Imaging Sciences 13 (3), 1630-1652, 2020 | 6 | 2020 |
| Revisiting subgradient method: Complexity and convergence beyond Lipschitz continuity X Li, L Zhao, D Zhu, AMC So Vietnam Journal of Mathematics, 1-21, 2024 | 5 | 2024 |
| Distributed stochastic optimization under a general variance condition K Huang, X Li, S Pu IEEE Transactions on Automatic Control, 2024 | 5 | 2024 |
| ReSync: Riemannian subgradient-based robust rotation synchronization H Liu, X Li, AMC So Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Nonconvex robust synchronization of rotations H Liu, Z Deng, X Li, S Chen, AMC So Proc. NeurIPS Annu. Workshop Optim. Mach. Learn.(OPT), 1-7, 2020 | 5 | 2020 |
