• 2579 Citations
20042021
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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

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

On fast convergence of proximal algorithms for SQRT-lasso optimization: Don’t worry about its nonsmooth loss function

Li, X., Jiang, H., Haupt, J., Arora, R., Liu, H., Hong, M. & Zhao, T., Jan 1 2019.

Research output: Contribution to conferencePaper

Learning systems
Tuning
Experiments

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

Exponentially weighted imitation learning for batched historical data

Wang, Q., Xiong, J., Han, L., Sun, P., Liu, H. & Zhang, T., Dec 1 2018, In : Advances in Neural Information Processing Systems. 31, 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. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 7638-7645 8 p. (35th International Conference on Machine Learning, ICML 2018; vol. 11).

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
7 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
4 Citations (Scopus)

Large covariance estimation through elliptical factor models

Fan, J., Liu, H. & Wang, W., Aug 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
6 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
6 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
1 Citation (Scopus)

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
1 Citation (Scopus)

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
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
6 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

Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma

Yang, Z., Balasubramanian, K., Wang, Z. & Liu, H., 2017, Proceedings of Advances in Neural Information Processing Systems 30 (NIPS 2017). Guyon, I., Von Luxburg, U. V. & Bengio, S. (eds.).

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

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
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)

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
11 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
5 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
19 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
11 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