9 citations · 34 across the 17 of their papers we have counts for
17 papers
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…
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…
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,…
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…
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…
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…
A Highly Efficient Algorithm for Solving Exclusive Lasso Problems
Meixia Lin, Yancheng Yuan, Defeng Sun +1
The exclusive lasso (also known as elitist lasso) regularizer has become popular recently due to its superior performance on intra-group feature selection. Its complex nature poses…
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…
A positive and moment-preserving Fourier spectral method
Zhenning Cai, Bo Lin, Meixia Lin
This paper presents a novel Fourier spectral method that utilizes optimization techniques to ensure the positivity and conservation of moments in the space of trigonometric polynom…
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
Remi Bardenet, Subhro Ghosh, Meixia Lin
Stochastic gradient descent (SGD) is a cornerstone of machine learning. When the number N of data items is large, SGD relies on constructing an unbiased estimator of the gradient o…
Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets
Subhroshekhar Ghosh, Meixia Lin, Dongfang Sun
Spectrograms are fundamental tools in time-frequency analysis, being the squared magnitude of the so-called short time Fourier transform (STFT). Signal analysis via spectrograms ha…
An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems
Meixia Lin, Defeng Sun, Kim-Chuan Toh
Shape-constrained convex regression problem deals with fitting a convex function to the observed data, where additional constraints are imposed, such as component-wise monotonicity…
Adaptive Sieving with PPDNA: Generating Solution Paths of Exclusive Lasso Models
Meixia Lin, Yancheng Yuan, Defeng Sun +1
The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on structured sparsity. Its complex nature poses diffic…
Estimation of sparse Gaussian graphical models with hidden clustering structure
Meixia Lin, Defeng Sun, Kim-Chuan Toh +1
Estimation of Gaussian graphical models is important in natural science when modeling the statistical relationships between variables in the form of a graph. The sparsity and clust…
Efficient algorithms for multivariate shape-constrained convex regression problems
Meixia Lin, Defeng Sun, Kim-Chuan Toh
Shape-constrained convex regression problem deals with fitting a convex function to the observed data, where additional constraints are imposed, such as component-wise monotonicity…
A dual Newton based preconditioned proximal point algorithm for exclusive lasso models
Meixia Lin, Defeng Sun, Kim-Chuan Toh +1
The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on group sparsity. Compared to the group lasso regulari…
Unravelling the mechanism of the semiconducting-like behavior and its relation to superconductivity in (CaFePtAs)PtAs
Run Yang, Yaomin Dai, Jia Yu +6
The temperature-dependence of the in-plane optical properties of (CaFePtAs)PtAs have been investigated for the undoped (0) parent compound, and…