2 citations · 5 across the 10 of their papers we have counts for
10 papers
Statistical Theory of Multi-stage Newton Iteration Algorithm for Online Continual Learning
Xinjia Lu, Chuhan Wang, Qian Zhao +2
We focus on the critical challenge of handling non-stationary data streams in online continual learning environments, where constrained storage capacity prevents complete retention…
Distributed quasi-Newton robust estimation under differential privacy
Chuhan Wang, Lixing Zhu, Xuehu Zhu
For distributed computing with Byzantine machines under Privacy Protection (PP) constraints, this paper develops a robust PP distributed quasi-Newton estimation, which only require…
Corrected kernel principal component analysis for model structural change detection
Luoyao Yu, Lixing Zhu, Ruoqing Zhu +1
This paper develops a method to detect model structural changes by applying a Corrected Kernel Principal Component Analysis (CKPCA) to construct the so-called central distribution…
Byzantine-robust distributed one-step estimation
Chuhan Wang, Xuehu Zhu, Lixing Zhu
This paper proposes a Robust One-Step Estimator(ROSE) to solve the Byzantine failure problem in distributed M-estimation when a moderate fraction of node machines experience Byzant…
Multiple change point detection in tensors
Jiaqi Huang, Junhui Wang, Xuehu Zhu +1
This paper proposes a criterion for detecting change structures in tensor data. To accommodate tensor structure with structural mode that is not suitable to be equally treated and…
Detecting multiple change points: a PULSE criterion
Wenbiao Zhao, Xuehu Zhu, Lixing Zhu
The research described herewith investigates detecting change points of means and of variances in a sequence of observations. The number of change points can be divergent at certai…
Adaptive-to-model hybrid of tests for regressions
Lingzhu Li, Xuehu Zhu, Lixing Zhu
In model checking for regressions, nonparametric estimation-based tests usually have tractable limiting null distributions and are sensitive to oscillating alternative models, but…
A projection-based adaptive-to-model test for regressions
Falong Tan, Xuehu Zhu, Lixing Zhu
A longstanding problem of existing empirical process-based tests for regressions is that when the number of covariates is greater than one, they either have no tractable limiting n…
An adaptive-to-model test for partially parametric single-index models
Xuehu Zhu, Xu Guo, Lixing Zhu
Residual marked empirical process-based tests are commonly used in regression models. However, they suffer from data sparseness in high-dimensional space when there are many covari…
Dimension reduction-based significance testing in nonparametric regression
Xuehu Zhu, Lixing Zhu
A dimension reduction-based adaptive-to-model test is proposed for significance of a subset of covariates in the context of a nonparametric regression model. Unlike existing local…