Cun-Hui Zhang

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Rutgers University

Department of Statistics and Biostatisticsat Rutgers University

Rank Determination in Tensor Factor Model

Factor model is an appealing and effective analytic tool for high-dimens...
11/13/2020 ∙ by Yuefeng Han, et al. ∙ 0 ∙ share

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Adaptive Estimation In High-Dimensional Additive Models With Multi-Resolution Group Lasso

In additive models with many nonparametric components, a number of regul...
11/13/2020 ∙ by Yisha Yao, et al. ∙ 0 ∙ share

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Tensor Factor Model Estimation by Iterative Projection

Tensor time series, which is a time series consisting of tensorial obser...
06/04/2020 ∙ by Yuefeng Han, et al. ∙ 0 ∙ share

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Confidence intervals for multiple isotonic regression and other monotone models

We consider the problem of constructing pointwise confidence intervals i...
01/20/2020 ∙ by Hang Deng, et al. ∙ 0 ∙ share

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Second order Poincaré inequalities and de-biasing arbitrary convex regularizers when p/n → γ

A new Central Limit Theorem (CLT) is developed for random variables of t...
12/26/2019 ∙ by Pierre C. Bellec, et al. ∙ 0 ∙ share

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Local Inference in Additive Models with Decorrelated Local Linear Estimator

Additive models, as a natural generalization of linear regression, have ...
07/30/2019 ∙ by Zijian Guo, et al. ∙ 0 ∙ share

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Limit distribution theory for multiple isotonic regression

We study limit distributions for the tuning-free max-min block estimator...
05/30/2019 ∙ by Qiyang Han, et al. ∙ 0 ∙ share

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Factor Models for High-Dimensional Tensor Time Series

Large tensor (multi-dimensional array) data are now routinely collected ...
05/18/2019 ∙ by Rong Chen, et al. ∙ 0 ∙ share

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Extreme Nonlinear Correlation for Multiple Random Variables and Stochastic Processes with Applications to Additive Models

The maximum correlation of functions of a pair of random variables is an...
04/29/2019 ∙ by Zijian Guo, et al. ∙ 0 ∙ share

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De-Biasing The Lasso With Degrees-of-Freedom Adjustment

This paper studies schemes to de-bias the Lasso in sparse linear regress...
02/24/2019 ∙ by Pierre C. Bellec, et al. ∙ 0 ∙ share

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Isotonic Regression in Multi-Dimensional Spaces and Graphs

In this paper we study minimax and adaptation rates in general isotonic ...
12/21/2018 ∙ by Hang Deng, et al. ∙ 0 ∙ share

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Second order Stein: SURE for SURE and other applications in high-dimensional inference

Stein's formula states that a random variable of the form z^ f(z) - div ...
11/09/2018 ∙ by Pierre C. Bellec, et al. ∙ 0 ∙ share

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Sorted Concave Penalized Regression

The Lasso is biased. Concave penalized least squares estimation (PLSE) t...
12/28/2017 ∙ by Long Feng, et al. ∙ 0 ∙ share

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Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries

In this article, we develop methods for estimating a low rank tensor fro...
11/14/2017 ∙ by Dong Xia, et al. ∙ 0 ∙ share

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Incoherent Tensor Norms and Their Applications in Higher Order Tensor Completion

In this paper, we investigate the sample size requirement for a general ...
06/10/2016 ∙ by Ming Yuan, et al. ∙ 0 ∙ share

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On Tensor Completion via Nuclear Norm Minimization

Many problems can be formulated as recovering a low-rank tensor. Althoug...
05/07/2014 ∙ by Ming Yuan, et al. ∙ 0 ∙ share

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Asymptotic normality and optimalities in estimation of large Gaussian graphical models

The Gaussian graphical model, a popular paradigm for studying relationsh...
09/24/2013 ∙ by Zhao Ren, et al. ∙ 0 ∙ share

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Exact Sparse Recovery with L0 Projections

Many applications concern sparse signals, for example, detecting anomali...
02/04/2013 ∙ by Ping Li, et al. ∙ 0 ∙ share

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Calibrated Elastic Regularization in Matrix Completion

This paper concerns the problem of matrix completion, which is to estima...
11/09/2012 ∙ by Tingni Sun, et al. ∙ 0 ∙ share

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One Permutation Hashing for Efficient Search and Learning

Recently, the method of b-bit minwise hashing has been applied to large-...
08/06/2012 ∙ by Ping Li, et al. ∙ 0 ∙ share

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Optimality of Graphlet Screening in High Dimensional Variable Selection

Consider a linear regression model where the design matrix X has n rows ...
04/29/2012 ∙ by Jiashun Jin, et al. ∙ 0 ∙ share

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Sparse Matrix Inversion with Scaled Lasso

We propose a new method of learning a sparse nonnegative-definite target...
02/13/2012 ∙ by Tingni Sun, et al. ∙ 0 ∙ share

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A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems

This paper develops a general theoretical framework to analyze structure...
01/16/2012 ∙ by Cun-Hui Zhang, et al. ∙ 0 ∙ share

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Estimation And Selection Via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

The ℓ_1-penalized method, or the Lasso, has emerged as an important tool...
12/29/2011 ∙ by Jian Huang, et al. ∙ 0 ∙ share

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A General Theory of Concave Regularization for High Dimensional Sparse Estimation Problems

Concave regularization methods provide natural procedures for sparse rec...
08/25/2011 ∙ by Cun-Hui Zhang, et al. ∙ 0 ∙ share

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Scaled Sparse Linear Regression

Scaled sparse linear regression jointly estimates the regression coeffic...
04/24/2011 ∙ by Tingni Sun, et al. ∙ 0 ∙ share

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Cun-Hui Zhang

Featured Co-authors

Tong Zhang

Ping Li

Qi Zhang

Jian Huang

Ming Yuan

Dong Xia

Rong Chen

Zhao Ren

Dan Yang

Qiyang Han

Harrison H. Zhou

Rank Determination in Tensor Factor Model

Adaptive Estimation In High-Dimensional Additive Models With Multi-Resolution Group Lasso

Tensor Factor Model Estimation by Iterative Projection

Confidence intervals for multiple isotonic regression and other monotone models

Second order Poincaré inequalities and de-biasing arbitrary convex regularizers when p/n → γ

Local Inference in Additive Models with Decorrelated Local Linear Estimator

Limit distribution theory for multiple isotonic regression

Factor Models for High-Dimensional Tensor Time Series

Extreme Nonlinear Correlation for Multiple Random Variables and Stochastic Processes with Applications to Additive Models

De-Biasing The Lasso With Degrees-of-Freedom Adjustment

Isotonic Regression in Multi-Dimensional Spaces and Graphs

Second order Stein: SURE for SURE and other applications in high-dimensional inference

Sorted Concave Penalized Regression

Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries

Incoherent Tensor Norms and Their Applications in Higher Order Tensor Completion

On Tensor Completion via Nuclear Norm Minimization

Asymptotic normality and optimalities in estimation of large Gaussian graphical models

Exact Sparse Recovery with L0 Projections

Calibrated Elastic Regularization in Matrix Completion

One Permutation Hashing for Efficient Search and Learning

Optimality of Graphlet Screening in High Dimensional Variable Selection

Sparse Matrix Inversion with Scaled Lasso

A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems

Estimation And Selection Via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

A General Theory of Concave Regularization for High Dimensional Sparse Estimation Problems

Scaled Sparse Linear Regression