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Minimax Rates of Community Detection in Stochastic Block Models
In this paper, we provide a general minimax theory for community detection for the stochastic block model. Expand
Achieving Optimal Misclassification Proportion in Stochastic Block Models
In this paper, we present a computationally feasible two-stage method that achieves optimal statistical performance in misclassification proportion for stochastic block model under weak regularity conditions. Expand
Optimality of Spectral Clustering for Gaussian Mixture Model
Spectral clustering is minimax optimal in the Gaussian Mixture Model with isotropic covariance matrix, when the number of clusters is fixed and the signal-to-noise ratio is large enough. Expand
Community Detection in Degree-Corrected Block Models
We propose a polynomial time algorithm to adaptively perform consistent and even asymptotically optimal community detection in Degree-Corrected Block Models (DCBMs). Expand
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection
The mean field variational Bayes method is becoming increasingly popular in statistics and machine learning. Expand
Partial Recovery for Top-$k$ Ranking: Optimality of MLE and Sub-Optimality of Spectral Method
Given partially observed pairwise comparison data generated by the Bradley-Terry-Luce (BTL) model, we study the problem of top-$k$ ranking. That is, to optimally identify the set of top-$k$ players.Expand
Iterative Algorithm for Discrete Structure Recovery
We propose a general modeling and algorithmic framework for discrete structure recovery that can be applied to a wide range of problems. Under this framework, we are able to study the recovery ofExpand
Achieving Optimal Misclassication Proportion in Stochastic Block Model
Community detection is a fundamental statistical problem in network data analysis. Many algorithms have been proposed to tackle this problem. Most of these algorithms are not guaranteed to achieveExpand
Adaptive clustering based on element-wised distance for distributed estimation over multi-task networks
An adaptive clustering method is proposed for distributed estimation that enables agents to distinguish between subneighbors that belong to the same cluster and those who belong to a different cluster. Expand
Bayesian Time Series Forecasting with Change Point and Anomaly Detection
We propose a novel state space time series model, with the capability to capture the structure of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods. Expand