• 2428 Citations
20042020
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Research Output 2004 2019

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
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

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
4 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

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

Fang, E. X., Liu, H. & Wang, M., Jan 1 2018, (Accepted/In press) In : Mathematical Programming.

Research output: Contribution to journalArticle

Cardinality Constraints
Spatial Model
Graphical Models
Convex Geometry
Geometry
4 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
4 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

Exponentially weighted imitation learning for batched historical data

Wang, Q., Xiong, J., Han, L., Sun, P., Liu, H. & Zhang, T., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 6288-6297 10 p.

Research output: Contribution to journalConference article

Reinforcement learning
Simulators
Trajectories

Feedback-based tree search for reinforcement learning

Jiang, D. R., Ekwedike, E. & Liu, H., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), Vol. 5. p. 3572-3590 19 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reinforcement learning
Artificial intelligence
Feedback
Decision trees
Learning algorithms
1 Citation (Scopus)

Fully decentralized multi-agent reinforcement learning with networked agents

Zhang, K., Yang, Z., Liu, H., Zhang, T. & Başar, T., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 9340-9371 32 p. (35th International Conference on Machine Learning, ICML 2018; vol. 13).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reinforcement learning
Learning algorithms
Telecommunication networks

Graphical nonconvex optimization via an adaptive convex relaxation

Sun, Q., Tan, K. M., Liu, H. & Zhang, T., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), Vol. 11. p. 7638-7645 8 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
2 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
1 Citation (Scopus)

Minimax-optimal privacy-preserving sparse PCA in distributed systems

Ge, J., Wang, Z., Wang, M. & Liu, H., Jan 1 2018, p. 1589-1598. 10 p.

Research output: Contribution to conferencePaper

Privacy Preserving
Minimax
Distributed Systems
Geometric Convergence
Privacy Preservation
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

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

Sketching method for large scale combinatorial inference

Sun, W. W., Lu, J. & Liu, H., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 10598-10607 10 p.

Research output: Contribution to journalConference article

Screening
Testing

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

Symmetry. saddle points, and global optimization landscape of nonconvex matrix factorization

Li, X., Haupt, J., Lu, J., Wang, Z., Arora, R., Liu, H. & Zhao, T., Oct 23 2018, 2018 Information Theory and Applications Workshop, ITA 2018. Institute of Electrical and Electronics Engineers Inc., 8503215. (2018 Information Theory and Applications Workshop, ITA 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Matrix Factorization
Global optimization
Saddlepoint
Factorization
Global Optimization

The edge density barrier: Computational-statistical tradeoffs in combinatorial inference

Lu, H., Cao, Y., Lu, J., Liu, H. & Wang, Z., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 5119-5148 30 p. (35th International Conference on Machine Learning, ICML 2018; vol. 7).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Testing
Computational complexity
Statistics
2017
19 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

Diffusion approximations for online principal component estimation and global convergence

Li, C. J., Wang, M., Liu, H. & Zhang, T., Jan 1 2017, In : Advances in Neural Information Processing Systems. 2017-December, p. 646-656 11 p.

Research output: Contribution to journalConference article

Principal component analysis
Eigenvalues and eigenfunctions
Markov processes
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
1 Citation (Scopus)

High-dimensional non-Gaussian single index models via thresholded score function estimation

Yang, Z., Balasubramanian, K. & Liu, H., Jan 1 2017, 34th International Conference on Machine Learning, ICML 2017. International Machine Learning Society (IMLS), Vol. 8. p. 5878-5887 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Experiments
16 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)

Learning non-Gaussian multi-index model via second-order Stein's method

Yang, Z., Balasubramanian, K., Wang, Z. & Liu, H., Jan 1 2017, In : Advances in Neural Information Processing Systems. 2017-December, p. 6098-6107 10 p.

Research output: Contribution to journalConference article

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

Parametric simplex method for sparse learning

Pang, H., Vanderbei, R., Liu, H. & Zhao, T., Jan 1 2017, In : Advances in Neural Information Processing Systems. 2017-December, p. 188-197 10 p.

Research output: Contribution to journalConference article

Linear regression
Discriminant analysis
Linear programming
Costs
Experiments
8 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
4 Citations (Scopus)

Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution

Han, F. & Liu, H., Feb 1 2017, In : Bernoulli. 23, 1, p. 23-57 35 p.

Research output: Contribution to journalReview article

Correlation Matrix
Statistical Analysis
Kendall's tau
Spectral Norm
Rate of Convergence
16 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
2 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
4 Citations (Scopus)

Agnostic estimation for misspecified phase retrieval models

Neykov, M., Wang, Z. & Liu, H., Jan 1 2016, In : Advances in Neural Information Processing Systems. p. 4096-4104 9 p.

Research output: Contribution to journalConference article

2 Citations (Scopus)

A lasso-based sparse knowledge gradient policy for sequential optimal learning

Li, Y., Liu, H. & Powell, W. B., Jan 1 2016, p. 417-425. 9 p.

Research output: Contribution to conferencePaper

Lasso
Gradient
Experiment
Experiments
Penalized Regression
5 Citations (Scopus)

An improved convergence analysis of cyclic block coordinate descent-type methods for strongly convex minimization

Li, X., Zhao, T., Arora, R., Liu, H. & Hong, M., Jan 1 2016, p. 491-499. 9 p.

Research output: Contribution to conferencePaper

Coordinate Descent
Convex Minimization
Convergence Analysis
Minimization Problem
Elastic Net
41 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
19 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