54 citations · 81 across the 10 of their papers we have counts for
10 papers
Exact low-dimensional reformulations for regularized spectral approximation
Shengxiang Deng, Xudong Li, Yangjing Zhang
We study exact low-dimensional reformulations for a regularized spectral approximation problem and its weighted variant. In the unweighted setting, weak-majorization monotonicity o…
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
Meixia Lin, Yangjing Zhang
We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices. Due to th…
On some perturbation properties of nonsmooth optimization on Riemannian manifolds with applications
Yuexin Zhou, Chao Ding, Yangjing Zhang
This paper presents a perturbation analysis framework for nonsmooth optimization on connected Riemannian manifolds to bridge the gap between the rapid development of algorithmic ap…
Strong Variational Sufficiency for Nonlinear Semidefinite Programming and its Implications
Shiwei Wang, Chao Ding, Yangjing Zhang +1
Strong variational sufficiency is a newly proposed property, which turns out to be of great use in the convergence analysis of multiplier methods. However, what this property impli…
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
Yangjing Zhang, Ying Cui, Bodhisattva Sen +1
In this paper we study the computation of the nonparametric maximum likelihood estimator (NPMLE) in multivariate mixture models. Our first approach discretizes this infinite dimens…
On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations
Hong T. M. Chu, Kim-Chuan Toh, Yangjing Zhang
Square-root (loss) regularized models have recently become popular in linear regression due to their nice statistical properties. Moreover, some of these models can be interpreted…
Learning Graph Laplacian with MCP
Yangjing Zhang, Kim-Chuan Toh, Defeng Sun
We consider the problem of learning a graph under the Laplacian constraint with a non-convex penalty: minimax concave penalty (MCP). For solving the MCP penalized graphical model,…
A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso Problems
Yangjing Zhang, Ning Zhang, Defeng Sun +1
Undirected graphical models have been especially popular for learning the conditional independence structure among a large number of variables where the observations are drawn inde…
An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems
Ning Zhang, Yangjing Zhang, Defeng Sun +1
Nowadays, analysing data from different classes or over a temporal grid has attracted a great deal of interest. As a result, various multiple graphical models for learning a collec…
An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems
Yangjing Zhang, Ning Zhang, Defeng Sun +1
The sparse group Lasso is a widely used statistical model which encourages the sparsity both on a group and within the group level. In this paper, we develop an efficient augmented…