7 citations · 11 across the 5 of their papers we have counts for
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stat.ML2018★ 2 cited
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…
stat.CO2018★ 1 cited
A Semi-Smooth Newton Algorithm for High-Dimensional Nonconvex Sparse Learning
Yueyong Shi, Jian Huang, Yuling Jiao +1
The smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) penalized regression models are two important and widely used nonconvex sparse learning tools t…