Yuejie Chi

Publications
Influence

Robust Spectral Compressed Sensing via Structured Matrix Completion

Y. Chen, Yuejie Chi
Computer Science, Mathematics
IEEE Transactions on Information Theory
30 April 2013

This paper explores the problem of spectral compressed sensing, which aims to recover a spectrally sparse signal from a small random subset of its time domain samples. Expand

Exact and Stable Covariance Estimation From Quadratic Sampling via Convex Programming

Y. Chen, Yuejie Chi, A. Goldsmith
Mathematics, Computer Science
IEEE Transactions on Information Theory
2 October 2013

We explore a quadratic (or rank-one) measurement model which imposes minimal memory requirements and low computational complexity during the sampling process, and is shown to be optimal in preserving various low-dimensional covariance structures. Expand

PETRELS: Parallel Subspace Estimation and Tracking by Recursive Least Squares From Partial Observations

Yuejie Chi, Yonina C. Eldar, A. R. Calderbank
Mathematics, Computer Science
IEEE Transactions on Signal Processing
26 July 2012

We consider the problem of reconstructing a data stream from a small subset of its entries, where the data is assumed to lie in a low-dimensional linear subspace, possibly corrupted by noise. Expand

Off-the-Grid Line Spectrum Denoising and Estimation With Multiple Measurement Vectors

Yuanxin Li, Yuejie Chi
Computer Science, Mathematics
IEEE Transactions on Signal Processing
10 August 2014

We study the problem of line spectrum denoising and estimation with an ensemble of spectrally-sparse signals composed of the same set of continuous-valued frequencies from their partial and noisy observations. Expand

Guaranteed Blind Sparse Spikes Deconvolution via Lifting and Convex Optimization

Yuejie Chi
Mathematics, Computer Science
IEEE Journal of Selected Topics in Signal…
9 June 2015

Neural recordings, returns from radars and sonars, images in astronomy and single-molecule microscopy can be modeled as a linear superposition of a small number of scaled and delayed copies of a band-limited or diffraction-limited point spread function, which is either determined by the nature or designed by the users. Expand

Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

Yuejie Chi, Yue M. Lu, Y. Chen
Computer Science, Engineering
IEEE Transactions on Signal Processing
25 September 2018

We present nonconvex optimization algorithms for low-rank matrix factorization that combine optimization and statistical models with performance guarantees. Expand

Compressive Two-Dimensional Harmonic Retrieval via Atomic Norm Minimization

Yuejie Chi, Y. Chen
Mathematics, Computer Science
IEEE Transactions on Signal Processing
1 February 2015

This paper is concerned with estimation of two-dimensional frequencies from partial time samples, which arises in many applications such as radar, inverse scattering, and super-resolution imaging. Expand

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution

C. Ma, Kaizheng Wang, Yuejie Chi, Y. Chen
Mathematics, Computer Science
Found. Comput. Math.
28 November 2017

This paper uncovers a striking phenomenon in nonconvex optimization: even in the absence of explicit regularization, gradient descent enforces proper regularization implicitly under various statistical models. Expand

Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval

Y. Chen, Yuejie Chi, Jianqing Fan, C. Ma
Mathematics, Computer Science
Math. Program.
21 March 2018

We investigate the efficacy of gradient descent (or Wirtinger flow) designed for the nonconvex least squares problem by exploiting the statistical models in analyzing optimization algorithms, via a leave-one-out approach that enables the decoupling of certain statistical dependency. Expand

Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations

Huishuai Zhang, Y. Zhou, Y. Liang, Yuejie Chi
Mathematics
25 May 2016

We study the phase retrieval problem, which solves quadratic system of equations, i.e., recovers a vector $\boldsymbol{x}\in \mathbb{R}^n$ from its magnitude measurements $y_i=|\langle… Expand

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