3 citations · 3 across the 6 of their papers we have counts for
7 papers
Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints
Halyun Jeong, Deanna Needell
The Kaczmarz method (KZ) and its variants, which are types of stochastic gradient descent (SGD) methods, have been extensively studied due to their simplicity and efficiency in sol…
Federated Gradient Matching Pursuit
Halyun Jeong, Deanna Needell, Jing Qin
Traditional machine learning techniques require centralizing all training data on one server or data hub. Due to the development of communication technologies and a huge amount of…
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate
Michael P. Friedlander, Halyun Jeong, Yaniv Plan +1
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence. Ther…
Approximate methods for phase retrieval via gauge duality
Ron Estrin, Yifan Sun, Halyun Jeong +1
We consider the problem of finding a low rank symmetric matrix satisfying a system of linear equations, as appears in phase retrieval. In particular, we solve the gauge dual formul…
Sub-Gaussian Matrices on Sets: Optimal Tail Dependence and Applications
Halyun Jeong, Xiaowei Li, Yaniv Plan +1
Random linear mappings are widely used in modern signal processing, compressed sensing and machine learning. These mappings may be used to embed the data into a significantly lower…
Polar Alignment and Atomic Decomposition
Zhenan Fan, Halyun Jeong, Yifan Sun +1
Structured optimization uses a prescribed set of atoms to assemble a solution that fits a model to data. Polarity, which extends the familiar notion of orthogonality from linear se…