10 citations · 50 across the 19 of their papers we have counts for
5 papers · 1 filter
Relative Entropy Gradient Sampler for Unnormalized Distributions
Xingdong Feng, Yuan Gao, Jian Huang +2
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms i…
Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly
Yuling Jiao, Dingwei Li, Min Liu +1
Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning. Nonconvex penalized least squares have been attrac…
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Jian Huang, Yuling Jiao, Lican Kang +3
Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size. In this paper, we develop a support detect…
A stochastic alternating minimizing method for sparse phase retrieval
Jianfeng Cai, Yuling Jiao, Xiliang Lu +1
Sparse phase retrieval plays an important role in many fields of applied science and thus attracts lots of attention. In this paper, we propose a \underline{sto}chastic alte\underl…
SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties
Jian Huang, Yuling Jiao, Xiliang Lu +2
We propose a semismooth Newton algorithm for pathwise optimization (SNAP) for the LASSO and Enet in sparse, high-dimensional linear regression. SNAP is derived from a suitable form…