205 citations · 822 across the 39 of their papers we have counts for
31 papers · 1 filter
Robust Regression with Adaptive Contamination in Response: Optimal Rates and Computational Barriers
Ilias Diakonikolas, Chao Gao, Daniel M. Kane +2
We study robust regression under a contamination model in which covariates are clean while the responses may be corrupted in an adaptive manner. Unlike the classical Huber's contam…
Computational Lower Bounds for Graphon Estimation via Low-degree Polynomials
Yuetian Luo, Chao Gao
Graphon estimation has been one of the most fundamental problems in network analysis and has received considerable attention in the past decade. From the statistical perspective, t…
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 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…
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…
Convergence Rates of Empirical Bayes Posterior Distributions: A Variational Perspective
Fengshuo Zhang, Chao Gao
We study the convergence rates of empirical Bayes posterior distributions for nonparametric and high-dimensional inference. We show that as long as the hyperparameter set is discre…
Model Repair: Robust Recovery of Over-Parameterized Statistical Models
Chao Gao, John Lafferty
A new type of robust estimation problem is introduced where the goal is to recover a statistical model that has been corrupted after it has been estimated from data. Methods are pr…
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…
Testing Equivalence of Clustering
Chao Gao, Zongming Ma
In this paper, we test whether two datasets share a common clustering structure. As a leading example, we focus on comparing clustering structures in two independent random samples…
Minimax rates in sparse, high-dimensional changepoint detection
Haoyang Liu, Chao Gao, Richard J. Samworth
We study the detection of a sparse change in a high-dimensional mean vector as a minimax testing problem. Our first main contribution is to derive the exact minimax testing rate ac…
Optimal estimation of variance in nonparametric regression with random design
Yandi Shen, Chao Gao, Daniela Witten +1
Consider the heteroscedastic nonparametric regression model with random design \begin{align*} Y_i = f(X_i) + V^{1/2}(X_i)\varepsilon_i, \quad i=1,2,\ldots,n, \end{align*} with $f(\…
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing
Chao Gao, Zongming Ma
This paper surveys some recent developments in fundamental limits and optimal algorithms for network analysis. We focus on minimax optimal rates in three fundamental problems of ne…
Density Estimation with Contaminated Data: Minimax Rates and Theory of Adaptation
Haoyang Liu, Chao Gao
This paper studies density estimation under pointwise loss in the setting of contamination model. The goal is to estimate at some with i.i.d. observatio…
Convergence Rates of Variational Posterior Distributions
Fengshuo Zhang, Chao Gao
We study convergence rates of variational posterior distributions for nonparametric and high-dimensional inference. We formulate general conditions on prior, likelihood, and variat…
Phase Transitions in Approximate Ranking
Chao Gao
We study the problem of approximate ranking from observations of pairwise interactions. The goal is to estimate the underlying ranks of objects from data through interactions o…
Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials
Chao Gao
We consider goodness-of-fit tests with i.i.d. samples generated from a categorical distribution . For a given , we test the null hypothesis whether $p…
On Estimation of Isotonic Piecewise Constant Signals
Chao Gao, Fang Han, Cun-Hui Zhang
Consider a sequence of real data points with underlying means . This paper starts from studying the setting that is both piecewise cons…
Robust Regression via Mutivariate Regression Depth
Chao Gao
This paper studies robust regression in the settings of Huber's -contamination models. We consider estimators that are maximizers of multivariate regression depth functions. The…
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…
Optimal Estimation and Completion of Matrices with Biclustering Structures
Chao Gao, Yu Lu, Zongming Ma +1
Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan (1972) where one seeks to cluster cases and variables simultaneously. Such struct…
A General Decision Theory for Huber's -Contamination Model
Mengjie Chen, Chao Gao, Zhao Ren
Today's data pose unprecedented challenges to statisticians. It may be incomplete, corrupted or exposed to some unknown source of contamination. We need new methods and theories to…
A General Framework for Bayes Structured Linear Models
Chao Gao, Aad W. van der Vaart, Harrison H. Zhou
High dimensional statistics deals with the challenge of extracting structured information from complex model settings. Compared with the growing number of frequentist methodologies…
Robust Covariance and Scatter Matrix Estimation under Huber's Contamination Model
Mengjie Chen, Chao Gao, Zhao Ren
Covariance matrix estimation is one of the most important problems in statistics. To accommodate the complexity of modern datasets, it is desired to have estimation procedures that…