Hui Zou | Academic Influence

Hui Zou's AcademicInfluence.com Rankings

Hui Zou

Computer Science

#4890

World Rank

#5162

Historical Rank

Data Mining

#84

World Rank

#84

Historical Rank

Machine Learning

#1104

World Rank

#1122

Historical Rank

computer-science Degrees

Hui Zou

Mathematics

#5034

World Rank

#7114

Historical Rank

Statistics

#370

World Rank

#438

Historical Rank

Measure Theory

#986

World Rank

#1280

Historical Rank

mathematics Degrees

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Computer Science
Mathematics

Hui Zou's Degrees

PhD Statistics University of California, Berkeley
Masters Statistics University of California, Berkeley
Bachelors Mathematics Peking University

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Why Is Hui Zou Influential?

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According to Wikipedia, Hui Zou is currently a professor of statistics at the University of Minnesota. Selected publications Honors and awards Fellow of the American Statistical Association, 2019Highly Cited Researcher in Mathematics, 2014, 2015, 2016, 2017, 2018Fellow, Institute of Mathematical Statistics, 2015.Institute of Mathematical Statistics Tweedie New Researcher Award, 2011National Science Foundation CAREER Award, 2009New Hot Paper in Mathematics, 2008Fast Breaking Paper in Mathematics, 2006

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Hui Zou's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Regularization and variable selection via the elastic net (2005) (15529)
The Adaptive Lasso and Its Oracle Properties (2006) (6487)
Sparse Principal Component Analysis (2006) (2891)
Multi-class AdaBoost ∗ (2009) (1428)
One-step Sparse Estimates in Nonconcave Penalized Likelihood Models. (2008) (1171)
On the “degrees of freedom” of the lasso (2007) (1005)
ON THE ADAPTIVE ELASTIC-NET WITH A DIVERGING NUMBER OF PARAMETERS. (2009) (696)
Composite quantile regression and the oracle Model Selection Theory (2008) (506)
Addendum: Regularization and variable selection via the elastic net (2005) (384)
NEW EFFICIENT ESTIMATION AND VARIABLE SELECTION METHODS FOR SEMIPARAMETRIC VARYING-COEFFICIENT PARTIALLY LINEAR MODELS. (2011) (301)
Sure independence screening for ultrahigh dimensional feature space Discussion (2008) (297)
STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION. (2012) (278)
The doubly regularized support vector machine (2006) (253)
Hybrid huberized support vector machines for microarray classification (2007) (250)
Regularized rank-based estimation of high-dimensional nonparanormal graphical models (2012) (246)
Regression Shrinkage and Selection via the Elastic Net , with Applications to Microarrays (2003) (224)
Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression (2010) (212)
A direct approach to sparse discriminant analysis in ultra-high dimensions (2012) (210)
A fast unified algorithm for solving group-lasso penalize learning problems (2015) (181)
Positive-Definite ℓ1-Penalized Estimation of Large Covariance Matrices (2012) (144)
The Kolmogorov filter for variable screening in high-dimensional binary classification (2013) (129)
Sparse precision matrix estimation via lasso penalized D-trace loss (2014) (124)
Structured variable selection and estimation (2009) (116)
The F ∞ -norm support vector machine (2008) (115)
The fused Kolmogorov filter: A nonparametric model-free screening method (2014) (104)
High dimensional semiparametric latent graphical model for mixed data (2014) (100)
Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection (2012) (100)
NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHER-CONSISTENT LOSSES. (2008) (93)
A Selective Overview of Sparse Principal Component Analysis (2018) (89)
Regularized simultaneous model selection in multiple quantiles regression (2008) (82)
Nonconcave penalized composite conditional likelihood estimation of sparse Ising models (2012) (77)
A cocktail algorithm for solving the elastic net penalized Cox’s regression in high dimensions (2013) (77)
Statistical Foundations of Data Science (2020) (74)
CoCoLasso for High-dimensional Error-in-variables Regression (2015) (73)
Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models (2015) (70)
An Improved 1-norm SVM for Simultaneous Classification and Variable Selection (2007) (61)
ADMM for High-Dimensional Sparse Penalized Quantile Regression (2018) (61)
Synthesis, characterization, DNA-binding and cleavage studies of [Ru(bpy)2(actatp)]2+ and [Ru(phen)2(actatp)]2+ (actatp=acenaphthereno[1,2-b]-1,4,8,9-tetraazariphenylence). (2003) (58)
A Penalized Maximum Likelihood Approach to Sparse Factor Analysis (2010) (54)
Multiclass Sparse Discriminant Analysis (2015) (51)
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning (2004) (50)
On Varying-coefficient Independence Screening for High-dimensional Varying-coefficient Models. (2014) (46)
A note on path-based variable selection in the penalized proportional hazards model (2008) (45)
An Efficient Algorithm for Computing the HHSVM and Its Generalizations (2013) (41)
Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models (2011) (40)
High-dimensional generalizations of asymmetric least squares regression and their applications (2016) (40)
Nonparametric multiple expectile regression via ER-Boost (2015) (37)
Another look at distance‐weighted discrimination (2018) (32)
Tweedie’s Compound Poisson Model With Grouped Elastic Net (2016) (30)
Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A mismatch negativity study (2016) (29)
Multitask Quantile Regression Under the Transnormal Model (2016) (29)
A Note On the Connection and Equivalence of Three Sparse Linear Discriminant Analysis Methods (2013) (26)
The Margin Vector , Admissible Loss and Multi-class Margin-based Classifiers (2005) (26)
A note on path-based variable selection in the penalized proportional hazards model (2008) (24)
Sparse Distance Weighted Discrimination (2015) (23)
Variable selection for non‐parametric quantile regression via smoothing spline analysis of variance (2013) (22)
Real-world Performance of Meta-analysis Methods for Double-Zero-Event Studies with Dichotomous Outcomes Using the Cochrane Database of Systematic Reviews (2019) (21)
Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces (2015) (20)
SURE-tuned tapering estimation of large covariance matrices (2013) (19)
Sparse semiparametric discriminant analysis (2015) (18)
Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A P3 study (2017) (16)
Rank-based tapering estimation of bandable correlation matrices (2013) (15)
A coordinate majorization descent algorithm for ℓ1 penalized learning (2014) (15)
An Alternating Manifold Proximal Gradient Method for Sparse Principal Component Analysis and Sparse Canonical Correlation Analysis (2020) (14)
Local CQR Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression. (2010) (13)
An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA (2019) (13)
Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates (2012) (13)
Efficient Global Approximation of Generalized Nonlinear ℓ1-Regularized Solution Paths and Its Applications (2009) (12)
Optimal estimation of sparse correlation matrices of semiparametric Gaussian copulas (2014) (11)
SURE Information Criteria for Large Covariance Matrix Estimation and Their Asymptotic Properties (2014) (11)
Sure independence screening and compressed random sensing (2011) (11)
Generalizing Koenker's distribution (2014) (10)
Sparse Composite Quantile Regression in Ultrahigh Dimensions With Tuning Parameter Calibration (2020) (10)
Structured variable selection in support vector machines (2007) (10)
Variable Selection for the Linear Support Vector Machine (2007) (9)
Semiparametric Sparse Discriminant Analysis in Ultra-High Dimensions (2013) (8)
Supplementary materials for “ Non-concave Penalized Composite Likelihood Estimation of Sparse Ising Models ” (2012) (8)
A simple method to improve principal components regression (2020) (8)
Minimax optimal estimation of general bandable covariance matrices (2013) (8)
A Multicategory Kernel Distance Weighted Discrimination Method for Multiclass Classification (2019) (7)
Classification with high dimensional features (2018) (7)
Applications of Peter Hall's martingale limit theory to estimating and testing high dimensional covariance matrices (2018) (6)
Model Building and Feature Selection with Genomic Data (2007) (6)
Multi-class support vector machine based on the minimization of class variance (2021) (6)
High Dimensional Inference (2020) (6)
Group Lasso Penalized Learning Using a Unified BMD Algorithm [R package gglasso version 1.5] (2020) (6)
Automatic Bayes Carpentry Using Unlabeled Data in Semi-Supervised Classification (6)
Semi-supervised dimensionality reduction via sparse locality preserving projection (2020) (6)
Nonparametric Variable Transformation in Sufficient Dimension Reduction (2015) (6)
NORM SUPPORT VECTOR MACHINE (2008) (5)
Sparse matrices in data analysis (2014) (5)
Aggregated expectile regression by exponential weighting (2018) (5)
Positive-Definite 1 -Penalized Estimation of Large (2012) (5)
On Sure Screening with Multiple Responses (2021) (5)
Bayesian High-Dimensional Regression for Change Point Analysis. (2019) (5)
The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response (2020) (5)
Semiparametric Sparse Discriminant Analysis (2013) (5)
A Boosted Tweedie Compound Poisson Model for Insurance Premium (2015) (4)
Discussion of “Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation” (2016) (4)
Asymptotic Properties of SURE Information Criteria for Large Covariance Matrices (2014) (3)
A Note on Cross-Validation for Lasso Under Measurement Errors (2019) (3)
Fast and Exact Leave-One-Out Analysis of Large-Margin Classifiers (2021) (2)
Internal Ratings and Loan Contracting: Evidence from a State-owned Bank around a Massive Economic Stimulus Programme (2019) (1)
Honest leave‐one‐out cross‐validation for estimating post‐tuning generalization error (2021) (1)
Variable selection. Editorial. (2013) (1)
Another Look at DWD: Thrifty Algorithm and Bayes Risk Consistency in RKHS (2015) (1)
Comment: Ridge Regression—Still Inspiring After 50 Years (2020) (1)
Bayesian Inference for High Dimensional Changing Linear Regression with Application to Minnesota House Price Index Data (2015) (1)
STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE (2014) (1)
Enveloped Huber Regression. (2020) (1)
High-dimensional Censored Regression via the Penalized Tobit Likelihood (2022) (1)
Expectile regression via deep residual networks (2020) (1)
Covariance Regularization and Graphical Models (2020) (0)
High-Dimensional Classification (2018) (0)
Multiple and Nonparametric Regression (2020) (0)
RANK-BASED ESTIMATION OF NONPARANORMAL GRAPHICAL MODELS 2543 TABLE 1 Testing for normality of the gene expression measurements in the Arabidposis thaliana data (2013) (0)
Supplementary Material: Nonparametric Variable Transformation in Sufficient Dimension Reduction (2015) (0)
THE DOUBLY REGULARIZED SUPPORT VECTOR MACHINE 591 model ( (2006) (0)
Generalized Linear Models and Penalized Likelihood (2020) (0)
Penalized Least Squares: Properties (2020) (0)
Discussion of minimax estimation of large covariance matrices under L1-Norm (2013) (0)
Statistica Sinica Preprint (2017) (0)
Friendship Quality Questionnaire—Chinese Version (2015) (0)
THE DOUBLY REGULARIZED SUPPORT (2006) (0)
Chapter 2 VARIABLE SELECTION FOR THE LINEAR SUPPORT VECTOR MACHINE (2006) (0)
Introduction to Penalized Least-Squares (2020) (0)
The nonparametric Box–Cox model for high-dimensional regression analysis (2023) (0)
Another look at distanceâ€ weighted discrimination (2018) (0)
Automatic Bias Correction Methods in Semi-supervised Learning (2019) (0)
Chapter 2 VARIABLE SELECTION FOR THE LINEAR SUPPORT VECTOR MACHINE (2018) (0)
Covariance Learning and Factor Models (2020) (0)
COCOLASSO FOR HIGH-DIMENSIONAL ERROR-IN-VARIABLES REGRESSION BY ABHIRUP DATTA (2017) (0)
Penalized M-estimators (2020) (0)
Density-Convoluted Support Vector Machines for High-Dimensional Classification (2023) (0)
Supplemental - Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces (2018) (0)
Multivariate Regression via Enhanced Response Envelope: Envelope Regularization and Double Descent (2023) (0)
High-Dimensional Variable Selection with Right Censored Length-biased Data (2020) (0)
Robust Rank Canonical Correlation Analysis for Multivariate Survival Data (0)
Cross-fitted Residual Regression for High Dimensional Heteroscedasticity Pursuit (2021) (0)
VARIABLE SELECTION FOR THE LINEAR SUPPORT VECTOR MACHINE (2020) (0)
Editorial (2013) (0)
Applications of Factor Models and PCA (2020) (0)
Discussion of “Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation”∗ (2016) (0)

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