378 citations · 2.2k across the 69 of their papers we have counts for
15 papers · 1 filter
Local Geometry of Nonconvex Spike Deconvolution from Low-Pass Measurements
Maxime Ferreira Da Costa, Yuejie Chi
Spike deconvolution is the problem of recovering the point sources from their convolution with a known point spread function, which plays a fundamental role in many sensing and ima…
Compressed Super-Resolution of Positive Sources
Maxime Ferreira Da Costa, Yuejie Chi
Atomic norm minimization is a convex optimization framework to recover point sources from a subset of their low-pass observations, or equivalently the underlying frequencies of a s…
On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution
Maxime Ferreira Da Costa, Yuejie Chi
The stability of spike deconvolution, which aims at recovering point sources from their convolution with a point spread function (PSF), is known to be related to the separation bet…
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…
Guaranteed Blind Sparse Spikes Deconvolution via Lifting and Convex Optimization
Yuejie Chi
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 delay…
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…
Joint Sparsity Recovery for Spectral Compressed Sensing
Yuejie Chi
Compressed Sensing (CS) is an effective approach to reduce the required number of samples for reconstructing a sparse signal in an a priori basis, but may suffer severely from the…
Exact and Stable Covariance Estimation from Quadratic Sampling via Convex Programming
Yuxin Chen, Yuejie Chi, Andrea Goldsmith
Statistical inference and information processing of high-dimensional data often require efficient and accurate estimation of their second-order statistics. With rapidly changing da…
Spectral Compressed Sensing via Structured Matrix Completion
Yuxin Chen, Yuejie Chi
The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples. Specifically, the object of interest with ambient dimension i…
Robust Spectral Compressed Sensing via Structured Matrix Completion
Yuxin Chen, Yuejie Chi
The paper explores the problem of \emph{spectral compressed sensing}, which aims to recover a spectrally sparse signal from a small random subset of its time domain samples. Th…
Compressive Demodulation of Mutually Interfering Signals
Yuejie Chi, Yao Xie, Robert Calderbank
Multi-User Detection is fundamental not only to cellular wireless communication but also to Radio-Frequency Identification (RFID) technology that supports supply chain management.…