22 citations · 22 across the 11 of their papers we have counts for
11 papers
Asymptotic Anytime-Valid Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
Sequential quantile inference is difficult under local differential privacy because every record is randomized before reaching the analyst and the limiting quantile variance depend…
High-Dimensional Assisted Learning for Vertically Distributed Data with Blockwise Missingness
Yuwen Long, Shuyuan Wu, Yin Xia
In multi-institutional studies, different parties hold distinct feature blocks for partially overlapping sets of individuals. Responses may also be missing for some records. In suc…
Adaptive Decentralized Federated Learning for Robust Optimization
Shuyuan Wu, Feifei Wang, Yuan Gao +2
In decentralized federated learning (DFL), the presence of abnormal clients, often caused by noisy or poisoned data, can significantly disrupt the learning process and degrade the…
Causal Inference for Network Autoregression Model: A Targeted Minimum Loss Estimation Approach
Yong Wu, Shuyuan Wu, Xinwei Sun +1
We study estimation of the average treatment effect (ATE) from a single network in observational settings with interference. The weak cross-unit dependence is modeled via an endoge…
Federated Learning of Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
In this paper, we investigate federated learning for quantile inference under local differential privacy (LDP). We propose an estimator based on local stochastic gradient descent (…
Decentralized EM Algorithm for Gaussian Mixtures under Data Heterogeneity and Partial Labeling
Xuetong Li, Shuyuan Wu, Bin Du +1
We systematically study several network-based Expectation-Maximization (EM) algorithms for the Gaussian mixture model within decentralized federated learning (DFL). Our theoretical…
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
Xuetong Li, Yuan Gao, Hong Chang +11
This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rap…
Bias-Correction for Privacy-Protected Spatial Autoregressive Models with Application to Restaurant Network Analysis
Danyang Huang, Ziyi Kong, Shuyuan Wu +1
Spatial autoregressive (SAR) models and their extensions are important tools for studying network effects. However, with an increasing emphasis on data privacy, data providers ofte…
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…
Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis with Limited Computational Resources
Shuyuan Wu, Xuening Zhu, Hansheng Wang
Modern statistical analysis often encounters datasets with large sizes. For these datasets, conventional estimation methods can hardly be used immediately because practitioners oft…
Network Gradient Descent Algorithm for Decentralized Federated Learning
Shuyuan Wu, Danyang Huang, Hansheng Wang
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it…