225 citations · 303 across the 17 of their papers we have counts for
17 papers
Privacy-Preserving Community Detection for Locally Distributed Multiple Networks
Xiao Guo, Xiang Li, Xiangyu Chang +1
Modern multi-layer networks are commonly stored and analyzed in a local and distributed fashion because of the privacy, ownership, and communication costs. The literature on the mo…
Subsampling-Based Modified Bayesian Information Criterion for Large-Scale Stochastic Block Models
Jiayi Deng, Danyang Huang, Xiangyu Chang +1
Identifying the number of communities is a fundamental problem in community detection, which has received increasing attention recently. However, rapid advances in technology have…
Snap-Shot Decentralized Stochastic Gradient Tracking Methods
Haishan Ye, Xiangyu Chang
In decentralized optimization, agents form a network and only communicate with their neighbors, which gives advantages in data ownership, privacy, and scalability. At the same…
On the efficacy of higher-order spectral clustering under weighted stochastic block models
Xiao Guo, Hai Zhang, Xiangyu Chang
Higher-order structures of networks, namely, small subgraphs of networks (also called network motifs), are widely known to be crucial and essential to the organization of networks.…
Statistical Estimation and Inference via Local SGD in Federated Learning
Xiang Li, Jiadong Liang, Xiangyu Chang +1
Federated Learning (FL) makes a large amount of edge computing devices (e.g., mobile phones) jointly learn a global model without data sharing. In FL, data are generated in a decen…
Explicit Superlinear Convergence Rates of The SR1 Algorithm
Haishan Ye, Dachao Lin, Zhihua Zhang +1
We study the convergence rate of the famous Symmetric Rank-1 (SR1) algorithm which has wide applications in different scenarios. Although it has been extensively investigated, SR1…
FedPower: Privacy-Preserving Distributed Eigenspace Estimation
Xiao Guo, Xiang Li, Xiangyu Chang +2
Eigenspace estimation is fundamental in machine learning and statistics, which has found applications in PCA, dimension reduction, and clustering, among others. The modern machine…
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks
Xiangyu Chang, Yingcong Li, Samet Oymak +1
Deep networks are typically trained with many more parameters than the size of the training dataset. Recent empirical evidence indicates that the practice of overparameterization n…
Kernel Interpolation of High Dimensional Scattered Data
Shao-Bo Lin, Xiangyu Chang, Xingping Sun
Data sites selected from modeling high-dimensional problems often appear scattered in non-paternalistic ways. Except for sporadic clustering at some spots, they become relatively f…
Randomized spectral co-clustering for large-scale directed networks
Xiao Guo, Yixuan Qiu, Hai Zhang +1
Directed networks are broadly used to represent asymmetric relationships among units. Co-clustering aims to cluster the senders and receivers of directed networks simultaneously. I…
Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Hai Zhang, Xiao Guo, Xiangyu Chang
Spectral clustering has been one of the widely used methods for community detection in networks. However, large-scale networks bring computational challenges to the eigenvalue deco…
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms
Xiangyu Chang, Shao-Bo Lin
In this paper, we propose an adaptive stopping rule for kernel-based gradient descent (KGD) algorithms. We introduce the empirical effective dimension to quantify the increments of…
Learning rates for classification with Gaussian kernels
Shao-Bo Lin, Jinshan Zeng, Xiangyu Chang
This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss. Our first result shows that for some lo…
Divide and Conquer Local Average Regression
Xiangyu Chang, Shaobo Lin, Yao Wang
The divide and conquer strategy, which breaks a massive data set into a se- ries of manageable data blocks, and then combines the independent results of data blocks to obtain a fin…
Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing
Yu Wang, Jinshan Zeng, Zhimin Peng +2
This paper studies the convergence of the adaptively iterative thresholding (AIT) algorithm for compressed sensing. We first introduce a generalized restricted isometry property (g…
Sparse K-Means with Penalty for High-Dimensional Data Clustering
Xiangyu Chang, Yu Wang, Rongjian Li +1
Sparse clustering, which aims to find a proper partition of an extremely high-dimensional data set with redundant noise features, has been attracted more and more interests in rece…
Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
Peter Bickel, David Choi, Xiangyu Chang +1
Variational methods for parameter estimation are an active research area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on…