5 citations · 8 across the 6 of their papers we have counts for
6 papers
A Survey of Numerical Algorithms that can Solve the Lasso Problems
Yujie Zhao, Xiaoming Huo
In statistics, the least absolute shrinkage and selection operator (Lasso) is a regression method that performs both variable selection and regularization. There is a lot of litera…
Hot-spots Detection in Count Data by Poisson Assisted Smooth Sparse Tensor Decomposition
Yujie Zhao, Xiaoming Huo, Yajun Mei
Count data occur widely in many bio-surveillance and healthcare applications, e.g., the numbers of new patients of different types of infectious diseases from different cities/coun…
Identification of Partial-Differential-Equations-Based Models from Noisy Data via Splines
Yujie Zhao, Xiaoming Huo, Yajun Mei
We propose a two-stage method called \textit{Spline Assisted Partial Differential Equation based Model Identification (SAPDEMI)} to identify partial differential equation (PDE)-bas…
Accelerate the Warm-up Stage in the Lasso Computation via a Homotopic Approach
Yujie Zhao, Xiaoming Huo
In optimization, it is known that when the objective functions are strictly convex and well-conditioned, gradient-based approaches can be extremely effective, e.g., achieving the e…
Rapid Detection of Hot-spots via Tensor Decomposition with applications to Crime Rate Data
Yujie Zhao, Hao Yan, Sarah Holte +1
We propose an efficient statistical method (denoted as SSR-Tensor) to robustly and quickly detect hot-spots that are sparse and temporal-consistent in a spatial-temporal dataset th…
Rapid Detection of Hot-spot by Tensor Decomposition with Application to Weekly Gonorrhea Data
Yujie Zhao, Hao Yan, Sarah E. Holte +2
In many bio-surveillance and healthcare applications, data sources are measured from many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications,…