17 citations · 72 across the 17 of their papers we have counts for
17 papers
Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network
Myung Cho, Lifeng Lai, Weiyu Xu
In this paper, we investigate the impact of imbalanced data on the convergence of distributed dual coordinate ascent in a tree network for solving an empirical loss minimization pr…
Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints
Jirong Yi, Myung Cho, Xiaodong Wu +2
In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than s…
Error Correction Codes for COVID-19 Virus and Antibody Testing: Using Pooled Testing to Increase Test Reliability
Jirong Yi, Myung Cho, Xiaodong Wu +2
We consider a novel method to increase the reliability of COVID-19 virus or antibody tests by using specially designed pooled testings. Instead of testing nasal swab or blood sampl…
Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach
Weiyu Xu, Jirong Yi, Soura Dasgupta +3
We consider the problem of recovering the superposition of distinct complex exponential functions from compressed non-uniform time-domain samples. Total Variation (TV) minimiza…
Fast dose optimization for rotating shield brachytherapy
Myung Cho, Xiaodong Wu, Hossein Dakhah +4
Purpose: To provide a fast computational method, based on the proximal graph solver (POGS) - a convex optimization solver using the alternating direction method of multipliers (ADM…
Distributed Dual Coordinate Ascent in General Tree Networks and Communication Network Effect on Synchronous Machine Learning
Myung Cho, Lifeng Lai, Weiyu Xu
Due to the big size of data and limited data storage volume of a single computer or a single server, data are often stored in a distributed manner. Thus, performing large-scale mac…
Phaseless super-resolution in the continuous domain
Myung Cho, Christos Thrampoulidis, Weiyu Xu +1
Phaseless super-resolution refers to the problem of superresolving a signal from only its low-frequency Fourier magnitude measurements. In this paper, we consider the phaseless sup…
Computable performance guarantees for compressed sensing matrices
Myung Cho, Kumar Vijay Mishra, Weiyu Xu
The null space condition for minimization in compressed sensing is a necessary and sufficient condition on the sensing matrices under which a sparse signal can be uniquely…
Block Iterative Reweighted Algorithms for Super-Resolution of Spectrally Sparse Signals
Myung Cho, Kumar Vijay Mishra, Jian-Feng Cai +1
We propose novel algorithms that enhance the performance of recovering unknown continuous-valued frequencies from undersampled signals. Our iterative reweighted frequency recovery…
Spectral Super-resolution With Prior Knowledge
Kumar Vijay Mishra, Myung Cho, Anton Kruger +1
We address the problem of super-resolution frequency recovery using prior knowledge of the structure of a spectrally sparse, undersampled signal. In many applications of interest,…
Super-resolution Line Spectrum Estimation with Block Priors
Kumar Vijay Mishra, Myung Cho, Anton Kruger +1
We address the problem of super-resolution line spectrum estimation of an undersampled signal with block prior information. The component frequencies of the signal are assumed to t…
Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional () Off-the-Grid Frequencies
Weiyu Xu, Jian-Feng Cai, Kumar Vijay Mishra +2
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-dom…
Off-The-Grid Spectral Compressed Sensing With Prior Information
Kumar Vijay Mishra, Myung Cho, Anton Kruger +1
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-dom…
Universally Elevating the Phase Transition Performance of Compressed Sensing: Non-Isometric Matrices are Not Necessarily Bad Matrices
Weiyu Xu, Myung Cho
In compressed sensing problems, minimization or Basis Pursuit was known to have the best provable phase transition performance of recoverable sparsity among polynomial-tim…
Precisely Verifying the Null Space Conditions in Compressed Sensing: A Sandwiching Algorithm
Myung Cho, Weiyu Xu
In this paper, we propose new efficient algorithms to verify the null space condition in compressed sensing (CS). Given an () CS matrix and a positive …
Toeplitz Matrix Based Sparse Error Correction in System Identification: Outliers and Random Noises
Weiyu Xu, Er-Wei Bai, Myung Cho
In this paper, we consider robust system identification under sparse outliers and random noises. In our problem, system parameters are observed through a Toeplitz matrix. All obser…
Outliers and Random Noises in System Identification: a Compressed Sensing Approach
Weiyu Xu, Erwei Bai, Myung Cho
In this paper, we consider robust system identification under sparse outliers and random noises. In this problem, system parameters are observed through a Toeplitz matrix. All obse…