Shuang Li

Title(s):

Office

Information

Education: 

Ph.D., Electrical Engineering, Colorado School of Mines (2020)

    B.S., Communication Engineering, Zhejiang University of Technology (2013)

Homepage: ece.iastate.edu/~lishuang/

Publications

Google Scholar Profile: https://scholar.google.com/citations?user=mzDw-lwAAAAJ&hl=en&citsig=AMstHGQ0-PBJbrzJ-d7jFdbX5rxwk8_Hrw

• Li, W. Swartworth, M. Takac, D. Needell, and R. M. Gower, “SP2: A second order stochastic Polyak method,” to appear in The Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023.
• Li, G. Tang, and M. B. Wakin, “Landscape correspondence of empirical and population risks in the eigendecomposition problem,” IEEE Transactions on Signal Processing, vol. 70, pp. 2985–2999, 2022.
• Li and Q. Li, “Local and global convergence of general Burer-Monteiro tensor optimizations,” The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), vol. 36, no. 9, pp. 10266- 10274, February 2022.
• Li, H. Mansour, and M. B. Wakin, “Recovery analysis of damped spectrally sparse signals and its relation to MUSIC,” Information and Inference: A Journal of the IMA, 2020.
• Li, Q. Li, Z. Zhu, G. Tang, and M. B. Wakin, “The global geometry of centralized and distributed low-rank matrix recovery without regularization,” IEEE Signal Processing Letters, vol. 27, pp. 1400-1404, 2020.
• Li, M. B. Wakin, and G. Tang, “Atomic norm denoising for complex exponentials with unknown waveform modulations,” IEEE Transactions on Information Theory, vol. 66, no. 6, pp. 3893-3913, 2020.
• Li, G. Tang, and M. B. Wakin, “The landscape of non-convex empirical risk with degenerate population risk,” The Thirty-third Conference on Neural Information Processing Systems (NeurIPS), pp. 3502-3512, December 2019.
• Li, D. Yang, G. Tang, and M. B. Wakin, “Atomic norm minimization for modal analysis from random and compressed samples,” IEEE Transactions on Signal Processing, vol. 66, no. 7, pp. 1817-1831, 2018.
• Li, Q. Li, G. Tang, and M. B. Wakin, “Geometry correspondence between empirical and population games,” The Bridging Game Theory and Deep Learning Workshop NeurIPS 2019 (Smooth Games Optimization and Machine Learning Series), Vancouver, Canada, December 2019.
• Li, Y. Xie, Q. Li, and G. Tang, “Cubic regularization for differentiable games,” The Bridging Game Theory and Deep Learning Workshop NeurIPS 2019 (Smooth Games Optimization and Machine Learning Series), Vancouver, Canada, December 2019.

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