22 citations · 84 across the 31 of their papers we have counts for
32 papers
Diagonal Attenuation: A Finite-Sample Correction for PCA
Qiang Sun
Principal component analysis (PCA) can rotate away from its population target when a covariance matrix is estimated from limited data. We introduce diagonal attenuation, which pres…
Online Generalized Sparse Regression: How Does Overparametrization Help?
Shuoguang Yang, Qiang Sun
Regularized sparse regression has been extensively studied in the offline setting, but online formulation remains relatively under-explored. This gap stems from four key challenges…
Bounded Projection Matrix Approximation with Applications to Community Detection
Zheng Zhai, Hengchao Chen, Qiang Sun
Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded co…
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices
Hengchao Chen, Xiang Li, Qiang Sun
Non-asymptotic statistical analysis is often missing for modern geometry-aware machine learning algorithms due to the possibly intricate non-linear manifold structure. This paper s…
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards
Xiang Li, Qiang Sun
This paper presents two algorithms, AdaOFUL and VARA, for online sequential decision-making in the presence of heavy-tailed rewards with only finite variances. For linear stochasti…
Quadratic Matrix Factorization with Applications to Manifold Learning
Zheng Zhai, Hengchao Chen, Qiang Sun
Matrix factorization is a popular framework for modeling low-rank data matrices. Motivated by manifold learning problems, this paper proposes a quadratic matrix factorization (QMF)…
Online Linearized LASSO
Shuoguang Yang, Yuhao Yan, Xiuneng Zhu +1
Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model. Existing ap…
Distributed Sparse Multicategory Discriminant Analysis
Hengchao Chen, Qiang Sun
This paper proposes a convex formulation for sparse multicategory linear discriminant analysis and then extend it to the distributed setting when data are stored across multiple si…
A provable two-stage algorithm for penalized hazards regression
Jianqing Fan, Wenyan Gong, Qiang Sun
From an optimizer's perspective, achieving the global optimum for a general nonconvex problem is often provably NP-hard using the classical worst-case analysis. In the case of Cox'…
Distributed Adaptive Huber Regression
Jiyu Luo, Qiang Sun, Wenxin Zhou
Distributed data naturally arise in scenarios involving multiple sources of observations, each stored at a different location. Directly pooling all the data together is often prohi…
Do we need to estimate the variance in robust mean estimation?
Qiang Sun
In this paper, we propose self-tuned robust estimators for estimating the mean of heavy-tailed distributions, which refer to distributions with only finite variances. Our approach…
Adaptive Capped Least Squares
Qiang Sun, Rui Mao, Wen-Xin Zhou
This paper proposes the capped least squares regression with an adaptive resistance parameter, hence the name, adaptive capped least squares regression. The key observation is, by…
Restricted Riemannian geometry for positive semidefinite matrices
A. Martina Neuman, Yuying Xie, Qiang Sun
We introduce the manifold of {\it restricted} positive semidefinite matrices of fixed rank , denoted . The manifold itself is an open and dense submanifo…
Supervised Principal Component Regression for Functional Responses with High Dimensional Predictors
Xinyi Zhang, Qiang Sun, Dehan Kong
We propose a supervised principal component regression method for relating functional responses with high dimensional predictors. Unlike the conventional principal component analys…
Bayesian high-dimensional linear regression with generic spike-and-slab priors
Bai Jiang, Qiang Sun
Spike-and-slab priors are popular Bayesian solutions for high-dimensional linear regression problems. Previous theoretical studies on spike-and-slab methods focus on specific prior…
Modeling Symmetric Positive Definite Matrices with An Application to Functional Brain Connectivity
Zhenhua Lin, Dehan Kong, Qiang Sun
In neuroscience, functional brain connectivity describes the connectivity between brain regions that share functional properties. Neuroscientists often characterize it by a time se…
Iteratively Reweighted -Penalized Robust Regression
Xiaoou Pan, Qiang Sun, Wen-Xin Zhou
This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonc…
Resistant convex clustering: How does the fusion penalty enhance resistantance?
Qiang Sun, Archer Gong Zhang, Chenyu Liu +1
Convex clustering is a convex relaxation of the -means and hierarchical clustering. It involves solving a convex optimization problem with the objective function being a squared…
Gaussian Approximations for Maxima of Random Vectors under -th Moments
Qiang Sun
We derive a Gaussian approximation result for the maximum of a sum of random vectors under -th moments. Our main theorem is abstract and nonasymptotic, and can be applied to…
Bayesian Factor-adjusted Sparse Regression
Jianqing Fan, Bai Jiang, Qiang Sun
This paper investigates the high-dimensional linear regression with highly correlated covariates. In this setup, the traditional sparsity assumption on the regression coefficients…
Exact Cluster Recovery via Classical Multidimensional Scaling
Anna Little, Yuying Xie, Qiang Sun
Classical multidimensional scaling is an important dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides…
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
Yuan Ke, Stanislav Minsker, Zhao Ren +2
We offer a survey of recent results on covariance estimation for heavy-tailed distributions. By unifying ideas scattered in the literature, we propose user-friendly methods that fa…
Modified Multidimensional Scaling and High Dimensional Clustering
Xiucai Ding, Qiang Sun
Multidimensional scaling is an important dimension reduction tool in statistics and machine learning. Yet few theoretical results characterizing its statistical performance exist,…
Robust Sparse Reduced Rank Regression in High Dimensions
Kean Ming Tan, Qiang Sun, Daniela Witten
We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex rela…