skscope: Fast Sparsity-Constrained Optimization in Python

1 code implementation • 27 Mar 2024 • Zezhi Wang, Jin Zhu, Peng Chen, Huiyang Peng, Xiaoke Zhang, Anran Wang, Yu Zheng, Junxian Zhu, Xueqin Wang

Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact.

A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models

no code implementations • 12 Sep 2023 • Borui Tang, Jin Zhu, Junxian Zhu, Xueqin Wang, Heping Zhang

SIMs provide an interpretable and flexible modeling framework for high-dimensional data, while best subset selection aims to find a sparse model from a large set of predictors.

Model Selection regression

Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique

no code implementations • 1 Aug 2023 • Junxian Zhu, Jin Zhu, Borui Tang, Xuanyu Chen, Hongmei Lin, Xueqin Wang

In high-dimensional generalized linear models, it is crucial to identify a sparse model that adequately accounts for response variation.

Computational Efficiency Variable Selection

abess: A Fast Best Subset Selection Library in Python and R

2 code implementations • 19 Oct 2021 • Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu

In addition, a user-friendly R library is available at the Comprehensive R Archive Network.

Model Selection Sparse Learning +1

A Splicing Approach to Best Subset of Groups Selection

1 code implementation • 23 Apr 2021 • Yanhang Zhang, Junxian Zhu, Jin Zhu, Xueqin Wang

Best subset of groups selection (BSGS) is the process of selecting a small part of non-overlapping groups to achieve the best interpretability on the response variable.

Variable Selection