5 papers
High-dimensional online learning via asynchronous decomposition: Non-divergent results, dynamic regularization, and beyond
Shixiang Liu, Zhifan Li, Hanming Yang +1
Existing high-dimensional online learning methods often face the challenge that their error bounds, or per-batch sample sizes, diverge as the number of data batches increases. To a…
Learning Joint Graphical Model with Computational Efficiency, Dynamic Regularization, and Adaptation
Shixiang Liu, Yanhang Zhang, Zhifan Li +1
Multi-sourced datasets are common in studies of variable interactions, for example, individual-level fMRI integration, cross-domain recommendation, etc, where each source induces a…
Exact recovery in the double sparse model: sufficient and necessary signal conditions
Shixiang Liu, Zhifan Li, Yanhang Zhang +1
The double sparse linear model, which has both group-wise and element-wise sparsity in regression coefficients, has attracted lots of attention recently. This paper establishes the…
Rethinking Hard Thresholding Pursuit: Full Adaptation and Sharp Estimation
Yanhang Zhang, Zhifan Li, Shixiang Liu +2
Hard Thresholding Pursuit (HTP) has aroused increasing attention for its robust theoretical guarantees and impressive numerical performance in non-convex optimization. In this pape…
A minimax optimal approach to high-dimensional double sparse linear regression
Yanhang Zhang, Zhifan Li, Shixiang Liu +1
In this paper, we focus our attention on the high-dimensional double sparse linear regression, that is, a combination of element-wise and group-wise sparsity. To address this probl…