553 citations · 754 across the 10 of their papers we have counts for
8 papers · 1 filter
Successive Ray Refinement and Its Application to Coordinate Descent for LASSO
Jun Liu, Zheng Zhao, Ruiwen Zhang
Coordinate descent is one of the most popular approaches for solving Lasso and its extensions due to its simplicity and efficiency. When applying coordinate descent to solving Lass…
Safe and Efficient Screening For Sparse Support Vector Machine
Zheng Zhao, Jun Liu
Screening is an effective technique for speeding up the training process of a sparse learning model by removing the features that are guaranteed to be inactive the process. In this…
A Safe Screening Rule for Sparse Logistic Regression
Jie Wang, Jiayu Zhou, Jun Liu +2
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts h…
Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods
Jie Wang, Jun Liu, Jieping Ye
Sparse learning has recently received increasing attention in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the…
Safe Screening With Variational Inequalities and Its Application to LASSO
Jun Liu, Zheng Zhao, Jie Wang +1
Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates th…
Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization
Jun Liu, Shuiwang Ji, Jieping Ye
The problem of joint feature selection across a group of related tasks has applications in many areas including biomedical informatics and computer vision. We consider the l2,1-nor…
Efficient L1/Lq Norm Regularization
Jun Liu, Jieping Ye
Sparse learning has recently received increasing attention in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the…
Fast Overlapping Group Lasso
Jun Liu, Jieping Ye
The group Lasso is an extension of the Lasso for feature selection on (predefined) non-overlapping groups of features. The non-overlapping group structure limits its applicability…