60 citations · 134 across the 15 of their papers we have counts for
10 papers · 1 filter
Robust Subgroup Analysis for Heterogeneous Censored Data
Zhaohui Xu, Daoji Li, Zemin Zheng
Subgroup analysis is important in practice because real-world data typically come from heterogeneous populations, where meaningful patterns can differ substantially across subpopul…
Simultaneous Heterogeneity and Reduced-rank Learning for Multivariate Response Regression
Jie Wu, Bo Zhang, Daoji Li +1
Heterogeneous data are now ubiquitous in many applications in which correctly identifying the subgroups from a heterogeneous population is critical. Although there is an increasing…
SOFARI-R: High-Dimensional Manifold-Based Inference for Latent Responses
Zemin Zheng, Xin Zhou, Jinchi Lv
Data reduction with uncertainty quantification plays a key role in various multi-task learning applications, where large numbers of responses and features are present. To this end,…
SOFARI: High-Dimensional Manifold-Based Inference
Zemin Zheng, Xin Zhou, Yingying Fan +1
Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the spar…
Parallel integrative learning for large-scale multi-response regression with incomplete outcomes
Ruipeng Dong, Daoji Li, Zemin Zheng
Multi-task learning is increasingly used to investigate the association structure between multiple responses and a single set of predictor variables in many applications. In the er…
Sequential scaled sparse factor regression
Zemin Zheng, Yang Li, Jie Wu +1
Large-scale association analysis between multivariate responses and predictors is of great practical importance, as exemplified by modern business applications including social med…
Nonsparse learning with latent variables
Zemin Zheng, Jinchi Lv, Wei Lin
As a popular tool for producing meaningful and interpretable models, large-scale sparse learning works efficiently when the underlying structures are indeed or close to sparse. How…
Scalable Interpretable Multi-Response Regression via SEED
Mohammad Taha Bahadori, Zemin Zheng, Yan Liu +1
Sparse reduced-rank regression is an important tool to uncover meaningful dependence structure between large numbers of predictors and responses in many big data applications such…
The constrained Dantzig selector with enhanced consistency
Yinfei Kong, Zemin Zheng, Jinchi Lv
The Dantzig selector has received popularity for many applications such as compressed sensing and sparse modeling, thanks to its computational efficiency as a linear programming pr…
High dimensional thresholded regression and shrinkage effect
Zemin Zheng, Yingying Fan, Jinchi Lv
High-dimensional sparse modeling via regularization provides a powerful tool for analyzing large-scale data sets and obtaining meaningful, interpretable models. The use of nonconve…