2 citations · 3 across the 6 of their papers we have counts for
6 papers
Quasi-Newton Updating for Large-Scale Distributed Learning
Shuyuan Wu, Danyang Huang, Hansheng Wang
Distributed computing is critically important for modern statistical analysis. Herein, we develop a distributed quasi-Newton (DQN) framework with excellent statistical, computation…
Subnetwork Estimation for Spatial Autoregressive Models in Large-scale Networks
Xuetong Li, Feifei Wang, Wei Lan +1
Large-scale networks are commonly encountered in practice (e.g., Facebook and Twitter) by researchers. In order to study the network interaction between different nodes of large-sc…
Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator
Haobo Qi, Feifei Wang, Hansheng Wang
We study here a fixed mini-batch gradient decent (FMGD) algorithm to solve optimization problems with massive datasets. In FMGD, the whole sample is split into multiple non-overlap…
On the asymptotic properties of a bagging estimator with a massive dataset
Yuan Gao, Riquan Zhang, Hansheng Wang
Bagging is a useful method for large-scale statistical analysis, especially when the computing resources are very limited. We study here the asymptotic properties of bagging estima…
Testing Sufficiency for Transfer Learning
Ziqian Lin, Yuan Gao, Feifei Wang +1
Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes the target data based statistical estimation very difficult. Then how…
Distributed Logistic Regression for Massive Data with Rare Events
Xuetong Li, Xuening Zhu, Hansheng Wang
Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression…