• 3056 Citations
20042020

Research output per year

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Research Output

2020

Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model

Lu, J., Kolar, M. & Liu, H., Jan 1 2020, (Accepted/In press) In : Journal of the American Statistical Association.

Research output: Contribution to journalArticle

Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming

Rosenblum, M., Fang, E. X. & Liu, H., Jul 1 2020, In : Journal of the Royal Statistical Society. Series B: Statistical Methodology. 82, 3, p. 749-772 24 p.

Research output: Contribution to journalArticle

2019

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 2019, In : Bernoulli. 25, 2, p. 1472-1503 32 p.

Research output: Contribution to journalArticle

1 Scopus citations

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

Combinatorial inference for graphical models

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

Research output: Contribution to journalArticle

2 Scopus citations

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

1 Scopus citations

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

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., 2019.

Research output: Contribution to conferencePaper

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

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

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

1 Scopus citations

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 2019, In : IEEE Transactions on Information Theory. 65, 6, p. 3489-3514 26 p., 8675509.

Research output: Contribution to journalArticle

2 Scopus citations
2018

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

3 Scopus citations

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 2018, In : Annals of Statistics. 46, 5, p. 1904-1931 28 p.

Research output: Contribution to journalArticle

6 Scopus citations

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

9 Scopus citations

Distributed testing and estimation under sparse high dimensional models

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

Research output: Contribution to journalArticle

14 Scopus citations

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

7 Scopus citations

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

1 Scopus citations

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), p. 3572-3590 19 p. (35th International Conference on Machine Learning, ICML 2018; vol. 5).

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

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

6 Scopus citations

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

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

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

Research output: Contribution to journalArticle

11 Scopus citations

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

10 Scopus citations

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

1 Scopus citations

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

7 Scopus citations

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

11 Scopus citations

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

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

2 Scopus citations

Pathwise coordinate optimization for sparse learning: Algorithm and theory

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

Research output: Contribution to journalArticle

7 Scopus citations

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

5 Scopus citations

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

3 Scopus citations

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

7 Scopus citations

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

1 Scopus citations

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

2017

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

39 Scopus citations

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

10 Scopus citations

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

2 Scopus citations

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

8 Scopus citations

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

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

1 Scopus citations

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

25 Scopus citations

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

2 Scopus citations

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

3 Scopus citations

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

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

17 Scopus citations

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

9 Scopus citations

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

25 Scopus citations