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20172021
most citedCompressed Sensing with Prior Information via Maximizing Correlation

3 citations · 4 across the 8 of their papers we have counts for

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cs.IT2021

A Sharp Algorithmic Analysis of Covariate Adjusted Precision Matrix Estimation with General Structural Priors

Xiao Lv, Wei Cui, Yulong Liu

In this paper, we present a sharp analysis for a class of alternating projected gradient descent algorithms which are used to solve the covariate adjusted precision matrix estimati…

cs.IT2021

Time-Data Tradeoffs in Structured Signals Recovery via the Proximal-Gradient Homotopy Method

Xiao Lv, Wei Cui, Yulong Liu

In this paper, we characterize data-time tradeoffs of the proximal-gradient homotopy method used for solving linear inverse problems under sub-Gaussian measurements. Our results ar…

cs.IT2021

Quantized Corrupted Sensing with Random Dithering

Zhongxing Sun, Wei Cui, Yulong Liu

Corrupted sensing concerns the problem of recovering a high-dimensional structured signal from a collection of measurements that are contaminated by unknown structured corruption a…

cs.IT2021

Phase Transitions in Recovery of Structured Signals from Corrupted Measurements

Zhongxing Sun, Wei Cui, Yulong Liu

This paper is concerned with the problem of recovering a structured signal from a relatively small number of corrupted random measurements. Sharp phase transitions have been numeri…

cs.IT2020

Spectrally Sparse Signal Recovery via Hankel Matrix Completion with Prior Information

Xu Zhang, Yulong Liu, Wei Cui

This paper studies the problem of reconstructing spectrally sparse signals from a small random subset of time domain samples via low-rank Hankel matrix completion with the aid of p…

cs.IT2020

Matrix Completion with Prior Subspace Information via Maximizing Correlation

Xu Zhang, Wei Cui, Yulong Liu

This paper studies the problem of completing a low-rank matrix from a few of its random entries with the aid of prior information. We suggest a strategy to incorporate prior inform…