6 papers
Efficient Group Lasso Regularized Rank Regression with Simulation-Based Tuning
Meixia Lin, Mengjiao Shi, Yunhai Xiao +1
High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods. To improve robustness,…
Latent Structural Categorical Matrix Completion with Application to Quasispecies Analysis
Qian Zhang, Meixia Lin
Matrix completion has been extensively studied for real-valued data, but existing methods are often limited in handling categorical variables. We propose LCMC, a double-loop optimi…
Inexact Accelerated Proximal Gradient Method Revisit: An Economical Variant via Shadow Points
Lei Yang, Meixia Lin
We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function via the inexact accelerated proximal gradient (APG) method. A key limitat…
On the B-subdifferential of proximal operators of affine-constrained regularizer
Xudong Li, Meixia Lin, Kim-Chuan Toh
In this work, we study the affine-constrained regularizers, which frequently arise in statistical and machine learning problems across a variety of applications, including…
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
Chengjing Wang, Peipei Tang, Wenling He +1
Graphical models have exhibited their performance in numerous tasks ranging from biological analysis to recommender systems. However, graphical models with hub nodes are computatio…
Adaptive sieving: A dimension reduction technique for sparse optimization problems
Yancheng Yuan, Meixia Lin, Defeng Sun +1
In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, whe…