6 papers · 1 filter
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…
Wasserstein distributionally robust optimization and its tractable regularization formulations
Hong T. M. Chu, Meixia Lin, Kim-Chuan Toh
We study a variety of Wasserstein distributionally robust optimization (WDRO) problems where the distributions in the ambiguity set are chosen by constraining their Wasserstein dis…