49 citations · 91 across the 6 of their papers we have counts for
7 papers
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
Cong Ma, Yuanxin Li, Yuejie Chi
Low-rank matrix estimation plays a central role in various applications across science and engineering. Recently, nonconvex formulations based on matrix factorization are provably…
Nonconvex Matrix Factorization from Rank-One Measurements
Yuanxin Li, Cong Ma, Yuxin Chen +1
We consider the problem of recovering low-rank matrices from random rank-one measurements, which spans numerous applications including covariance sketching, phase retrieval, quantu…
Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent
Yuanxin Li, Yuejie Chi, Huishuai Zhang +1
Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent…
Low-Rank Positive Semidefinite Matrix Recovery from Corrupted Rank-One Measurements
Yuanxin Li, Yue Sun, Yuejie Chi
We study the problem of estimating a low-rank positive semidefinite (PSD) matrix from a set of rank-one measurements using sensing vectors composed of i.i.d. standard Gaussian entr…
Stable Separation and Super-Resolution of Mixture Models
Yuanxin Li, Yuejie Chi
We consider simultaneously identifying the membership and locations of point sources that are convolved with different band-limited point spread functions, from the observation of…
Super-Resolution of Mutually Interfering Signals
Yuanxin Li, Yuejie Chi
We consider simultaneously identifying the membership and locations of point sources that are convolved with different low-pass point spread functions, from the observation of thei…
Off-the-Grid Line Spectrum Denoising and Estimation with Multiple Measurement Vectors
Yuanxin Li, Yuejie Chi
Compressed Sensing suggests that the required number of samples for reconstructing a signal can be greatly reduced if it is sparse in a known discrete basis, yet many real-world si…