71 citations · 163 across the 19 of their papers we have counts for
19 papers
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…
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…
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…
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…
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…
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…
Covariance Matrix Estimation from Correlated Sub-Gaussian Samples
Xu Zhang, Wei Cui, Yulong Liu
This paper studies the problem of estimating a covariance matrix from correlated sub-Gaussian samples. We consider using the correlated sample covariance matrix estimator to approx…
Recovery of Structured Signals From Corrupted Non-Linear Measurements
Zhongxing Sun, Wei Cui, Yulong Liu
This paper studies the problem of recovering a structured signal from a relatively small number of corrupted non-linear measurements. Assuming that signal and corruption are contai…
Localization from Incomplete Euclidean Distance Matrix: Performance Analysis for the SVD-MDS Approach
Huan Zhang, Yulong Liu, Hong Lei
Localizing a cloud of points from noisy measurements of a subset of pairwise distances has applications in various areas, such as sensor network localization and reconstruction of…
Covariance Matrix Estimation from Linearly-Correlated Gaussian Samples
Wei Cui, Xu Zhang, Yulong Liu
Covariance matrix estimation concerns the problem of estimating the covariance matrix from a collection of samples, which is of extreme importance in many applications. Classical r…
Phase Transition of Convex Programs for Linear Inverse Problems with Multiple Prior Constraints
Huan Zhang, Yulong Liu, Hong Lei
A sharp phase transition emerges in convex programs when solving the linear inverse problem, which aims to recover a structured signal from its linear measurements. This paper stud…
Recovery of Structured Signals with Prior Information via Maximizing Correlation
Xu Zhang, Wei Cui, Yulong Liu
This paper considers the problem of recovering a structured signal from a relatively small number of noisy measurements with the aid of a similar signal which is known beforehand.…
Stable Recovery of Structured Signals From Corrupted Sub-Gaussian Measurements
Jinchi Chen, Yulong Liu
This paper studies the problem of accurately recovering a structured signal from a small number of corrupted sub-Gaussian measurements. We consider three different procedures to re…
Compressed Sensing with Prior Information via Maximizing Correlation
Xu Zhang, Wei Cui, Yulong Liu
Compressed sensing (CS) with prior information concerns the problem of reconstructing a sparse signal with the aid of a similar signal which is known beforehand. We consider a new…
On the Phase Transition of Corrupted Sensing
Huan Zhang, Yulong Liu, Hong Lei
In \cite{FOY2014}, a sharp phase transition has been numerically observed when a constrained convex procedure is used to solve the corrupted sensing problem. In this paper, we pres…
Corrupted Sensing with Sub-Gaussian Measurements
Jinchi Chen, Yulong Liu
This paper studies the problem of accurately recovering a structured signal from a small number of corrupted sub-Gaussian measurements. We consider three different procedures to re…
Fast thresholding algorithms with feedbacks for sparse signal recovery
Shidong Li, Yulong Liu, Tiebin Mi
We provide another framework of iterative algorithms based on thresholding, feedback and null space tuning for sparse signal recovery arising in sparse representations and compress…
Performance Analysis of -synthesis with Coherent Frames
Yulong Liu, Shidong Li, Tiebin Mi
Signals with sparse frame representations comprise a much more realistic model of nature than that with orthonomal bases. Studies about the signal recovery associated with such spa…
Compressed Sensing with General Frames via Optimal-dual-based -analysis
Yulong Liu, Tiebin Mi, Shidong Li
Compressed sensing with sparse frame representations is seen to have much greater range of practical applications than that with orthonormal bases. In such settings, one approach t…