323 citations · 1k across the 34 of their papers we have counts for
35 papers
Separation-Free Spectral Super-Resolution via Convex Optimization
Zai Yang, Yi-Lin Mo, Gongguo Tang +1
Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these…
Quantum state tomography with tensor train cross approximation
Alexander Lidiak, Casey Jameson, Zhen Qin +4
It has been recently shown that a state generated by a one-dimensional noisy quantum computer is well approximated by a matrix product operator with a finite bond dimension indepen…
Error Analysis of Tensor-Train Cross Approximation
Zhen Qin, Alexander Lidiak, Zhexuan Gong +3
Tensor train decomposition is widely used in machine learning and quantum physics due to its concise representation of high-dimensional tensors, overcoming the curse of dimensional…
Landscape Correspondence of Empirical and Population Risks in the Eigendecomposition Problem
Shuang Li, Gongguo Tang, Michael B. Wakin
Spectral methods include a family of algorithms related to the eigenvectors of certain data-generated matrices. In this work, we are interested in studying the geometric landscape…
The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery without Regularization
Shuang Li, Qiuwei Li, Zhihui Zhu +2
Low-rank matrix recovery is a fundamental problem in signal processing and machine learning. A recent very popular approach to recovering a low-rank matrix X is to factorize it as…
Support Recovery for Sparse Signals with Unknown Non-stationary Modulation
Youye Xie, Michael B. Wakin, Gongguo Tang
The problem of estimating a sparse signal from low dimensional noisy observations arises in many applications, including super resolution, signal deconvolution, and radar imaging.…