1. Median-Truncated Gradient Descent: A Robust and Scalable Nonconvex Approach for Signal Estimation
Y. Chi, Y. Li, H. Zhang, and Y. Liang, Compressed Sensing and Its Applications, Springer, Birkhauser, 2019.
R. Baghbaderani, Y. Li, S. Wang, Publication/Patent Number: US20230115081A1 Publication Date: 2023-04-13.
1. Beyond Procrustes: Balancing-free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
C. Ma, Y. Li, and Y. Chi, IEEE Transactions on Signal Processing, vol. 69, pp. 867-877, 2021.
2. Nonconvex Matrix Factorization from Rank-One Measurements [Arxiv]
Y. Li, C. Ma, Y. Chen, and Y. Chi, IEEE Transactions on Information Theory, vol. 67, no. 3, pp. 1928-1950, 2021.
3. Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent [Arxiv]
Y. Li, Y. Chi, H. Zhang and Y. Liang, Information and Inference: A Journal of the IMA, vol. 9, no. 2, pp. 289-325, 2020.
4. Stable Separation and Super-Resolution of Mixture Models [Arxiv]
Y. Li and Y. Chi, Applied and Computational Harmonic Analysis, vol. 46, no. 1, pp. 1-39, 2019.
5. Low-Rank Positive Semidefinite Matrix Recovery from Corrupted Rank-One Measurements [Arxiv]
Y. Li, Y. Sun and Y. Chi, IEEE Transactions on Signal Processing, vol. 65, no. 2, pp. 397-408, 2017.
6. Off-the-Grid Line Spectrum Denoising and Estimation with Multiple Measurement Vectors [Arxiv]
Y. Li and Y. Chi, IEEE Transactions on Signal Processing, vol. 64, no. 5, pp. 1257-1269, 2016.
R. Baghbaderani, Y. Li, S. Wang and H. Qi, Winter Conference on Applications of Computer Vision (WACV), 2024.
2. Beyond Procrustes: Balancing-free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
C. Ma, Y. Li and Y. Chi, In 2019 53rd Asilomar Conference on Signals, Systems, and Computers, pp. 721-725. IEEE, 2019.
3. Solving Quadratic Equations via Amplitude-Based Nonconvex Optimization
V. Monardo, Y. Li, and Y. Chi, in Acoustics, Speech and Signal Processing (ICASSP), 2019 IEEE International Conference on, pp. 5526-5530. IEEE, 2019.
4. Nonconvex Matrix Factorization from Rank-One Measurements
Y. Li, C. Ma, Y. Chen and Y. Chi, Proceedings of Machine Learning Research, PMLR 89:1496-1505, 2019 (AISTATS 2019).
5. Non-Convex Low-rank Matrix Recovery from Corrupted Random Linear Measurements
Y. Li, Y. Chi, H. Zhang and Y. Liang, in Sampling Theory and Applications (SampTA), 2017 International Conference on, pp. 134-137. IEEE, 2017.
6. Performance Bounds for Modal Analysis using Sparse Linear Arrays
Y. Li, A. Pezeshki, L. L. Scharf, and Y. Chi, in Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, vol. 10211, p. 102110I. International Society for Optics and Photonics, 2017.
7. Outlier-Robust Recovery of Low-Rank Positive Semidefinite Matrices from Magnitude Measurements
Y. Sun, Y. Li, and Y. Chi, in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, pp. 4069-4073. IEEE, 2016.
8. Blind Calibration of Multi-Channel Samplers using Sparse Recovery
Y. Li, Y. He, Y. Chi and Y. M. Lu, in Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 IEEE 6th International Workshop on, pp. 33-36. IEEE, 2015.
9. Super-Resolution of Mutually Interfering Signals [Arxiv]
Y. Li and Y. Chi, in Information Theory (ISIT), 2015 IEEE International Symposium on, pp. 984-988. IEEE, 2015.
10. Parameter Estimation for Mixture Models via Convex Optimization
Y. Li and Y. Chi, in Sampling Theory and Applications (SampTA), 2015 International Conference on, pp. 483-487. IEEE, 2015.
Y. Li and Y. Chi, in Statistical Signal Processing (SSP), 2014 IEEE Workshop on, pp. 384-387. IEEE, 2014.
12. Automatic Content Classification of Digital Modulation Signals without Binary Sequence Recovery
Y. Li, Y. Liu, H. Meng, and X. Wang, in Signal Processing (ICSP), 2012 IEEE 11th International Conference on, vol. 2, pp. 1217-1221. IEEE, 2012.