5 papers
Sparse Deep Learning Models with the Regularization
Lixin Shen, Rui Wang, Yuesheng Xu +1
Sparse neural networks are highly desirable in deep learning in reducing its complexity. The goal of this paper is to study how choices of regularization parameters influence the s…
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
Qi Zhang, Yi Zhou, Ashley Prater-Bennette +2
Distributionally robust optimization (DRO) is a powerful framework for training robust models against data distribution shifts. This paper focuses on constrained DRO, which has an…
Computing Proximity Operators of Scale and Signed Permutation Invariant Functions
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen
This paper investigates the computation of proximity operators for scale and signed permutation invariant functions. A scale-invariant function remains unchanged under uniform scal…
Hyperparameter Estimation for Sparse Bayesian Learning Models
Feng Yu, Lixin Shen, Guohui Song
Sparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL…
A Successive Two-stage Method for Sparse Generalized Eigenvalue Problems
Qia Li, Jianmin Liao, Lixin Shen +1
The Sparse Generalized Eigenvalue Problem (sGEP), a pervasive challenge in statistical learning methods including sparse principal component analysis, sparse Fisher's discriminant…