8 citations · 19 across the 10 of their papers we have counts for
14 papers
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…
Row-wise Fusion Regularization: An Interpretable Personalized Federated Learning Framework in Large-Scale Scenarios
Runlin Zhou, Letian Li, Zemin Zheng
We study personalized federated learning for multivariate responses where client models are heterogeneous yet share variable-level structure. Existing entry-wise penalties ignore c…
Navigating Sparsities in High-Dimensional Linear Contextual Bandits
Rui Zhao, Zihan Chen, Zemin Zheng
High-dimensional linear contextual bandit problems remain a significant challenge due to the curse of dimensionality. Existing methods typically consider either the model parameter…
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…