54 citations · 95 across the 16 of their papers we have counts for
18 papers
Tightness of SDP and Burer-Monteiro Factorization for Phase Synchronization in High-Noise Regime
Anderson Ye Zhang
We study the difference between the maximum likelihood estimation (MLE) and its semi-definite programming (SDP) relaxation for the phase synchronization problem, where latent p…
Misspecified Maximum Likelihood Estimation for Non-Uniform Group Orbit Recovery
Sheng Xu, Anderson Ye Zhang, Amit Singer
We study maximum likelihood estimation (MLE) in the generalized group orbit recovery model, where each observation is generated by applying a random group action and a known, fixed…
Optimal and Private Learning from Human Response Data
Duc Nguyen, Anderson Y. Zhang
Item response theory (IRT) is the study of how people make probabilistic decisions, with diverse applications in education testing, recommendation systems, among others. The Rasch…
Efficient and Accurate Learning of Mixtures of Plackett-Luce Models
Duc Nguyen, Anderson Y. Zhang
Mixture models of Plackett-Luce (PL) -- one of the most fundamental ranking models -- are an active research area of both theoretical and practical significance. Most previously pr…
A Spectral Approach to Item Response Theory
Duc Nguyen, Anderson Zhang
The Rasch model is one of the most fundamental models in \emph{item response theory} and has wide-ranging applications from education testing to recommendation systems. In a univer…
Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering
Anderson Y. Zhang, Harrison H. Zhou
The singular subspaces perturbation theory is of fundamental importance in probability and statistics. It has various applications across different fields. We consider two arbitrar…
Uncertainty quantification in the Bradley-Terry-Luce model
Chao Gao, Yandi Shen, Anderson Y. Zhang
The Bradley-Terry-Luce (BTL) model is a benchmark model for pairwise comparisons between individuals. Despite recent progress on the first-order asymptotics of several popular proc…
Optimal Orthogonal Group Synchronization and Rotation Group Synchronization
Chao Gao, Anderson Y. Zhang
We study the statistical estimation problem of orthogonal group synchronization and rotation group synchronization. The model is $Y_{ij} = Z_i^* Z_j^{*T} + σW_{ij}\in\mathbb{R}^{d\…
Optimal Full Ranking from Pairwise Comparisons
Pinhan Chen, Chao Gao, Anderson Y. Zhang
We consider the problem of ranking players from partial pairwise comparison data under the Bradley-Terry-Luce model. For the first time in the literature, the minimax rate of t…
Optimal Clustering in Anisotropic Gaussian Mixture Models
Xin Chen, Anderson Y. Zhang
We study the clustering task under anisotropic Gaussian Mixture Models where the covariance matrices from different clusters are unknown and are not necessarily the identical matri…
SDP Achieves Exact Minimax Optimality in Phase Synchronization
Chao Gao, Anderson Y. Zhang
We study the phase synchronization problem with noisy measurements , where is an -dimensional complex unit-modulus vector and …
Exact Minimax Estimation for Phase Synchronization
Chao Gao, Anderson Y. Zhang
We study the phase synchronization problem with measurements , where is an -dimensional complex unit-modulus vector and is a c…
Iterative Algorithm for Discrete Structure Recovery
Chao Gao, Anderson Y. Zhang
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…
Optimality of Spectral Clustering in the Gaussian Mixture Model
Matthias Löffler, Anderson Y. Zhang, Harrison H. Zhou
Spectral clustering is one of the most popular algorithms to group high dimensional data. It is easy to implement and computationally efficient. Despite its popularity and successf…
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection
Anderson Y. Zhang, Harrison H. Zhou
The mean field variational Bayes method is becoming increasingly popular in statistics and machine learning. Its iterative Coordinate Ascent Variational Inference algorithm has bee…
Community Detection in Degree-Corrected Block Models
Chao Gao, Zongming Ma, Anderson Y. Zhang +1
Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of cluster…
Minimax Rates of Community Detection in Stochastic Block Models
Anderson Y. Zhang, Harrison H. Zhou
Recently network analysis has gained more and more attentions in statistics, as well as in computer science, probability, and applied mathematics. Community detection for the stoch…
Achieving Optimal Misclassification Proportion in Stochastic Block Model
Chao Gao, Zongming Ma, Anderson Y. Zhang +1
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 guara…