7 citations · 11 across the 6 of their papers we have counts for
6 papers
Sparsity-Constraint Optimization via Splicing Iteration
Jin Zhu, Junxian Zhu, Zezhi Wang +3
Sparsity-constrained optimization underlies many problems in signal processing, statistics, and machine learning. State-of-the-art hard-thresholding (HT) algorithms rely on an appr…
skscope: Fast Sparsity-Constrained Optimization in Python
Zezhi Wang, Jin Zhu, Peng Chen +5
Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad im…
Reconstruct Ising Model with Global Optimality via SLIDE
Xuanyu Chen, Jin Zhu, Junxian Zhu +2
The reconstruction of interaction networks between random events is a critical problem arising from statistical physics and politics, sociology, biology, psychology, and beyond. Th…
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models
Borui Tang, Jin Zhu, Junxian Zhu +2
Analysis of high-dimensional data has led to increased interest in both single index models (SIMs) and the best-subset selection. SIMs provide an interpretable and flexible modelin…
Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique
Junxian Zhu, Jin Zhu, Borui Tang +3
In high-dimensional generalized linear models, it is crucial to identify a sparse model that adequately accounts for response variation. Although the best subset section has been w…
abess: A Fast Best Subset Selection Library in Python and R
Jin Zhu, Xueqin Wang, Liyuan Hu +5
We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classifi…