• 2518 Citations
20042021
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Research Output 2004 2019

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Article
2019
1 Citation (Scopus)

An extreme-value approach for testing the equality of large U-statistic based correlation matrices

Zhou, C., Han, F., Zhang, X. S. & Liu, H., May 1 2019, In : Bernoulli. 25, 2, p. 1472-1503 32 p.

Research output: Contribution to journalArticle

U-statistics
Correlation Matrix
Extreme Values
Equality
Testing

Blessing of massive scale: spatial graphical model estimation with a total cardinality constraint approach

Fang, E. X., Liu, H. & Wang, M., Jul 1 2019, In : Mathematical Programming. 176, 1-2, p. 175-205 31 p.

Research output: Contribution to journalArticle

Cardinality Constraints
Spatial Model
Graphical Models
Convex Geometry
Geometry
1 Citation (Scopus)

Combinatorial inference for graphical models

Neykov, M., Lu, J. & Liu, H., Apr 1 2019, In : Annals of Statistics. 47, 2, p. 795-827 33 p.

Research output: Contribution to journalArticle

Graphical Models
Testing
Graph in graph theory
Lower bound
Graph Connectivity

Efficient, certifiably optimal clustering with applications to latent variable graphical models

Eisenach, C. & Liu, H., Jul 1 2019, In : Mathematical Programming. 176, 1-2, p. 137-173 37 p.

Research output: Contribution to journalArticle

Latent Variable Models
Semidefinite Programming
Graphical Models
Computational complexity
Semidefinite Programming Relaxation

Layer-wise learning strategy for nonparametric tensor product smoothing spline regression and graphical models

Tan, K. M., Lu, J., Zhang, T. & Liu, H., Aug 1 2019, In : Journal of Machine Learning Research. 20

Research output: Contribution to journalArticle

Tensor Product Splines
Smoothing Splines
Multivariate Functions
Learning Strategies
Graphical Models

Picasso: A sparse learning library for high dimensional data analysis in R and python

Ge, J., Li, X., Jiang, H., Liu, H., Zhang, T., Wang, M. & Zhao, T., Mar 1 2019, In : Journal of Machine Learning Research. 20

Research output: Contribution to journalArticle

Python
High-dimensional Data
Data analysis
Linear regression
Logistics
1 Citation (Scopus)

Property testing in high-dimensional ising models

Neykov, M. & Liu, H., Jan 1 2019, In : Annals of Statistics. 47, 5, p. 2472-2503 32 p.

Research output: Contribution to journalArticle

Property Testing
Ising Model
High-dimensional
Ferromagnet
Graph in graph theory

Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization

Li, X., Lu, J., Arora, R., Haupt, J., Liu, H., Wang, Z. & Zhao, T., Jun 1 2019, In : IEEE Transactions on Information Theory. 65, 6, p. 3489-3514 26 p., 8675509.

Research output: Contribution to journalArticle

Global optimization
Factorization
neural network
Learning systems
guarantee
2018
1 Citation (Scopus)

A convex formulation for high-dimensional sparse sliced inverse regression

Tan, K. M., Wang, Z., Zhang, T., Liu, H. & Cook, R. D., Dec 1 2018, In : Biometrika. 105, 4, p. 769-782 14 p.

Research output: Contribution to journalArticle

Sliced Inverse Regression
Convex optimization
Covariates
High-dimensional
multipliers
5 Citations (Scopus)

A new perspective on robust M-estimation: Finite sample theory and applications to dependence-adjusted multiple testing

Zhou, W. X., Bose, K., Fan, J. & Liu, H., Oct 1 2018, In : Annals of Statistics. 46, 5, p. 1904-1931 28 p.

Research output: Contribution to journalArticle

M-estimation
Multiple Testing
Normal Approximation
Parameter Tuning
Estimator
5 Citations (Scopus)

A unified theory of confidence regions and testing for high-dimensional estimating equations

Neykov, M., Ning, Y., Liu, J. S. & Liu, H., Aug 1 2018, In : Statistical Science. 33, 3, p. 427-443 17 p.

Research output: Contribution to journalArticle

Confidence Region
Estimating Equation
High-dimensional
Testing
Likelihood
5 Citations (Scopus)

Distributed testing and estimation under sparse high dimensional models

Battey, H., Fan, J., Liu, H., Lu, J. & Zhu, Z., Jun 1 2018, In : Annals of Statistics. 46, 3, p. 1352-1382 31 p.

Research output: Contribution to journalArticle

Divide-and-conquer Algorithm
High-dimensional
Estimator
Testing
Hypothesis Testing
5 Citations (Scopus)

ECA: High-Dimensional Elliptical Component Analysis in Non-Gaussian Distributions

Han, F. & Liu, H., Jan 2 2018, In : Journal of the American Statistical Association. 113, 521, p. 252-268 17 p.

Research output: Contribution to journalArticle

High-dimensional
Covariance matrix
Optimal Rate of Convergence
Eigenvector
Rank Statistics

Heterogeneity adjustment with applications to graphical model inference

Fan, J., Liu, H., Wang, W. & Zhu, Z., Jan 1 2018, In : Electronic Journal of Statistics. 12, 2, p. 3908-3952 45 p.

Research output: Contribution to journalArticle

Open Access
Graphical Models
Adjustment
Justify
Appeal
Batch
5 Citations (Scopus)

I-LAMM for sparse learning: Simultaneous control of algorithmic complexity and statistical error

Fan, J., Liu, H., Sun, Q. & Zhang, T., Apr 1 2018, In : Annals of Statistics. 46, 2, p. 814-841 28 p.

Research output: Contribution to journalArticle

Algorithmic Complexity
Convex Program
Tolerance
Penalized Quasi-likelihood
Iteration
3 Citations (Scopus)

Large covariance estimation through elliptical factor models

Fan, J., Liu, H. & Wang, W., Aug 1 2018, In : Annals of Statistics. 46, 4, p. 1383-1414 32 p.

Research output: Contribution to journalArticle

Covariance Estimation
Factor Models
Thresholding
Complement
Covariance Matrix Estimation
1 Citation (Scopus)

Max-norm optimization for robust matrix recovery

Fang, E. X., Liu, H., Toh, K. C. & Zhou, W. X., Jan 1 2018, In : Mathematical Programming. 167, 1, p. 5-35 31 p.

Research output: Contribution to journalArticle

Recovery
Sampling
Low-rank Matrices
Norm
Optimization
5 Citations (Scopus)

Near-optimal stochastic approximation for online principal component estimation

Li, C. J., Wang, M., Liu, H. & Zhang, T., Jan 1 2018, In : Mathematical Programming. 167, 1, p. 75-97 23 p.

Research output: Contribution to journalArticle

Optimal Approximation
Stochastic Approximation
Principal Components
Principal component analysis
Principal Component Analysis

On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization

Li, X., Zhao, T., Arora, R., Liu, H. & Hong, M., Apr 1 2018, In : Journal of Machine Learning Research. 18, p. 1-24 24 p.

Research output: Contribution to journalArticle

Coordinate Descent
Convex Minimization
Minimization Problem
Gradient Descent Method
Elastic Net
1 Citation (Scopus)

On semiparametric exponential family graphical models

Yang, Z., Ning, Y. & Liu, H., Oct 1 2018, In : Journal of Machine Learning Research. 19, p. 1-59 59 p.

Research output: Contribution to journalArticle

Exponential Family
Graphical Models
Mixed Data
Score Test
Hypothesis Test
4 Citations (Scopus)

Pathwise coordinate optimization for sparse learning: Algorithm and theory

Zhao, T., Liu, H. & Zhang, T., Feb 1 2018, In : Annals of Statistics. 46, 1, p. 180-218 39 p.

Research output: Contribution to journalArticle

Learning Theory
Learning Algorithm
Optimization
Optimization Algorithm
Active Set

Post-regularization inference for time-varying nonparanormal graphical models

Lu, J., Kolar, M. & Liu, H., Apr 1 2018, In : Journal of Machine Learning Research. 18, p. 1-78 78 p.

Research output: Contribution to journalArticle

Graphical Models
Regularization
Time-varying
High-dimensional
Correlation Matrix
2 Citations (Scopus)

RWEN: Response-weighted elastic net for prediction of chemosensitivity of cancer cell lines

Basu, A., Mitra, R., Liu, H., Schreiber, S. L. & Clemons, P. A., Oct 1 2018, In : Bioinformatics. 34, 19, p. 3332-3339 8 p.

Research output: Contribution to journalArticle

Elastic Net
Cancer
Cells
Cell Line
Sample Size

Sparse generalized eigenvalue problem: optimal statistical rates via truncated Rayleigh flow

Tan, K. M., Wang, Z., Liu, H. & Zhang, T., Nov 1 2018, In : Journal of the Royal Statistical Society. Series B: Statistical Methodology. 80, 5, p. 1057-1086 30 p.

Research output: Contribution to journalArticle

Generalized Eigenvalue Problem
Rayleigh
Nonconvex Optimization
Statistical Model
Sufficient Dimension Reduction
2017
21 Citations (Scopus)

A general theory of hypothesis tests and confidence regions for sparse high dimensional models

Ning, Y. & Liu, H., Feb 1 2017, In : Annals of Statistics. 45, 1, p. 158-195 38 p.

Research output: Contribution to journalArticle

Test of Hypothesis
Confidence Region
High-dimensional
Score Function
Additive Hazards Model
5 Citations (Scopus)

A likelihood ratio framework for high-dimensional semiparametric regression

Ning, Y., Zhao, T. & Liu, H., Dec 1 2017, In : Annals of Statistics. 45, 6, p. 2299-2327 29 p.

Research output: Contribution to journalArticle

Semiparametric Regression
Likelihood Ratio
High-dimensional
Generalized Linear Model
Data analysis
4 Citations (Scopus)

Distribution-free tests of independence in high dimensions

Han, F., Chen, S. & Liu, H., Dec 1 2017, In : Biometrika. 104, 4, p. 813-828 16 p.

Research output: Contribution to journalArticle

Test of Independence
Distribution-free Test
Higher Dimensions
Test Statistic
Statistics
17 Citations (Scopus)

High dimensional semiparametric latent graphical model for mixed data

Fan, J., Liu, H., Ning, Y. & Zou, H., Mar 1 2017, In : Journal of the Royal Statistical Society. Series B: Statistical Methodology. 79, 2, p. 405-421 17 p.

Research output: Contribution to journalArticle

Mixed Data
Latent Variables
Graphical Models
High-dimensional
Binary Variables
1 Citation (Scopus)

Mining Massive Amounts of Genomic Data: A Semiparametric Topic Modeling Approach

Fang, E. X., Li, M. D., Jordan, M. I. & Liu, H., Jul 3 2017, In : Journal of the American Statistical Association. 112, 519, p. 921-932 12 p.

Research output: Contribution to journalArticle

Transcription Factor
Genomics
Mining
Tumor
Modeling
9 Citations (Scopus)

Provable sparse tensor decomposition

Sun, W. W., Lu, J., Liu, H. & Cheng, G., Jun 1 2017, In : Journal of the Royal Statistical Society. Series B: Statistical Methodology. 79, 3, p. 899-916 18 p.

Research output: Contribution to journalArticle

Tensor Decomposition
High-dimensional
Tensor
Decomposition Method
Decompose
18 Citations (Scopus)

Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions

Wang, M., Fang, E. X. & Liu, H., Jan 1 2017, In : Mathematical Programming. 161, 1-2, p. 419-449 31 p.

Research output: Contribution to journalArticle

Descent Algorithm
Gradient Algorithm
Gradient Descent
Expected Value
Value Function
3 Citations (Scopus)

Testing and confidence intervals for high dimensional proportional hazards models

Fang, E. X., Ning, Y. & Liu, H., Nov 1 2017, In : Journal of the Royal Statistical Society. Series B: Statistical Methodology. 79, 5, p. 1415-1437 23 p.

Research output: Contribution to journalArticle

Partial Likelihood
Proportional Hazards Model
Hazard Function
Hypothesis Testing
Confidence interval
9 Citations (Scopus)

TIGER: A tuning-insensitive approach for optimally estimating gaussian graphical models

Liu, H. & Wang, L., Jan 1 2017, In : Electronic Journal of Statistics. 11, 1, p. 241-294 54 p.

Research output: Contribution to journalArticle

Gaussian Model
Graphical Models
Tuning
Parameter Tuning
Minimax
2016
2 Citations (Scopus)
Path Following
Thresholding
Shrinkage
Graph in graph theory
Graph
4 Citations (Scopus)

A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs

Kang, J., Bowman, F. D. B., Mayberg, H. & Liu, H., Nov 1 2016, In : NeuroImage. 141, p. 431-441 11 p.

Research output: Contribution to journalArticle

Major Depressive Disorder
Depression
Healthy Volunteers
Magnetic Resonance Imaging
Brain
45 Citations (Scopus)

An overview of the estimation of large covariance and precision matrices

Fan, J., Liao, Y. & Liu, H., Feb 1 2016, In : Econometrics Journal. 19, 1, p. C1-C32

Research output: Contribution to journalArticle

Financial data
Economic data
Conditional correlation
Covariance matrix
Statistical analysis
22 Citations (Scopus)

A partially linear framework for massive heterogeneous data

Zhao, T., Cheng, G. & Liu, H., Jan 1 2016, In : Annals of Statistics. 44, 4, p. 1400-1437 38 p.

Research output: Contribution to journalArticle

Asymptotic distribution
Plug-in Estimator
Kernel Regression
Ridge Regression
Optimal Bound

A semiparametric graphical modelling approach for large-scale equity selection

Liu, H., Mulvey, J. & Zhao, T., Jul 2 2016, In : Quantitative Finance. 16, 7, p. 1053-1067 15 p.

Research output: Contribution to journalArticle

Modeling
Equity
Copula
Rebalancing
Harvest
1 Citation (Scopus)

Identifying economic regimes: Reducing downside risks for university endowments and foundations

Mulvey, J. M. & Liu, H., Sep 1 2016, In : Journal of Portfolio Management. 43, 1, p. 100-108 9 p.

Research output: Contribution to journalArticle

Contagion
Economics
Endowments
Downside risk
Economic conditions
18 Citations (Scopus)

Joint estimation of multiple graphical models from high dimensional time series

Qiu, H., Han, F., Liu, H. & Caffo, B., Mar 1 2016, In : Journal of the Royal Statistical Society. Series B: Statistical Methodology. 78, 2, p. 487-504 18 p.

Research output: Contribution to journalArticle

Multiple Models
Graphical Models
High-dimensional
Time series
Parameter Estimation
3 Citations (Scopus)

Replicates in high dimensions, with applications to latent variable graphical models

Tan, K. M., Ning, Y., Witten, D. M. & Liu, H., Dec 1 2016, In : Biometrika. 103, 4, p. 761-777 17 p.

Research output: Contribution to journalArticle

Latent Variable Models
Graphical Models
Design of experiments
Higher Dimensions
Research Design

Revisiting compressed sensing: exploiting the efficiency of simplex and sparsification methods

Vanderbei, R., Lin, K., Liu, H. & Wang, L., Sep 1 2016, In : Mathematical Programming Computation. 8, 3, p. 253-269 17 p.

Research output: Contribution to journalArticle

Compressed sensing
Compressed Sensing
Interior Point Method
Kronecker Product
Simplex Method
1 Citation (Scopus)

Robust inference of risks of large portfolios

Fan, J., Han, F., Liu, H. & Vickers, B., Oct 1 2016, In : Journal of Econometrics. 194, 2, p. 298-308 11 p.

Research output: Contribution to journalArticle

Robust inference
Stock market
Financial returns
Estimator
Confidence
2 Citations (Scopus)

Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions

Shou, H., Shinohara, R. T., Liu, H., Reich, D. S. & Crainiceanu, C. M., Apr 2 2016, In : Journal of Computational and Graphical Statistics. 25, 2, p. 570-588 19 p.

Research output: Contribution to journalArticle

Image Enhancement
Null hypothesis
Multiple Sclerosis
Hypothesis Testing
Mixture Distribution
2 Citations (Scopus)

Sparse median graphs estimation in a high-dimensional semiparametric model

Han, F., Han, X., Liu, H. & Caffo, B., Sep 1 2016, In : Annals of Applied Statistics. 10, 3, p. 1397-1426 30 p.

Research output: Contribution to journalArticle

Median Graph
Sparse Graphs
Semiparametric Model
Numerical analysis
High-dimensional
2015
16 Citations (Scopus)

A direct estimation of high dimensional stationary vector autoregressions

Han, F., Lu, H. & Liu, H., Dec 1 2015, In : Journal of Machine Learning Research. 16, p. 3115-3150 36 p.

Research output: Contribution to journalArticle

Vector Autoregression
Vector Autoregressive Model
High-dimensional
Lasso
Time series
9 Citations (Scopus)

Calibrated multivariate regression with application to neural semantic basis discovery

Liu, H., Wang, L. & Zhao, T., Aug 1 2015, In : Journal of Machine Learning Research. 16, p. 1579-1606 28 p.

Research output: Contribution to journalArticle

Multivariate Regression
Semantics
Parameter estimation
Brain
High-dimensional
30 Citations (Scopus)

Generalized alternating direction method of multipliers: new theoretical insights and applications

Fang, E. X., He, B., Liu, H. & Yuan, X., Jun 18 2015, In : Mathematical Programming Computation. 7, 2, p. 149-187 39 p.

Research output: Contribution to journalArticle

Method of multipliers
Alternating Direction Method
Iteration
Statistical Learning
Linearization
4 Citations (Scopus)

Glmgraph: An R package for variable selection and predictive modeling of structured genomic data

Chen, L., Liu, H., Kocher, J. P. A., Li, H. & Chen, J., Jul 3 2015, In : Bioinformatics. 31, 24, p. 3991-3993 3 p.

Research output: Contribution to journalArticle

Predictive Modeling
Variable Selection
Genomics
Bacterial Structures
Penalty
8 Citations (Scopus)

Optimal feature selection in high-dimensional discriminant analysis

Kolar, M. & Liu, H., Feb 1 2015, In : IEEE Transactions on Information Theory. 61, 2, p. 1063-1083 21 p., 6985722.

Research output: Contribution to journalArticle

dimensional analysis
discriminant analysis
Discriminant analysis
Feature extraction
equivalence