Han Liu @ Computer Science, Northwestern University

Selected Papers before 2022

Five papers are selected to represent our research before 2022, a complete list of publications is available at my Google Scholar page.

Acknowledgement. The research results presented on this page are supported by the grants NSF IIS 1546482-BIGDATA, NIH R01MH102339, NSF IIS1408910, NSF IIS1332109, NIH R01GM083084, NIH R01HG06841.

Selected Paper No.1:

Combinatorial Inference for Graphical Models

Matey Neykov, Junwei Lu and Han Liu

The Annals of Statistics , Volume 47, Number 2 (2019), pp795-827.

Blurb. This paper proposes a new family of combinatorial inference problems which aim at testing the global structural properties of high dimenisonal graphical models. Our main contribution is to develop a unified theory to characterize the fundamental limits and efficient algorithms for a large family of combinatorial inference problems.

Selected Paper No.2:

Property Testing in High Dimensional Ising models

Matey Neykov and Han Liu

The Annals of Statistics, Accepted. 2019.

Blurb. This paper explores the information-theoretic limitations of graph property testing in zero-field Ising models. In particular, we extended the theoretical results for combinatorial inference from Gaussian graphical models to Ising graphical models. Both information-theoretic upper and lower bounds are developed.

Selected Paper No.3:

Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory

Tuo Zhao, Han Liu, and Tong Zhang

The Annals of Statistics, Volume 46, Number 1 (2018), pp180-218.

Blurb. This paper develops a model-based statistical optimization theory to analyze the pathwise coordinate optimization algorithms.We solved an open problem on providing a matehmatical theory to justify the superior performance of the pathwise coordinate optimization strategies for convex/nonconvex sparse learning problems.

Selected Paper No.4:

A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models

Yang Ning and Han Liu

The Annals of Statistics. Volume 45, Number 1 (2017), pp158-195.

Blurb. This paper considers both hypothesis tests and confidence regions for generic penalized M-estimators. Unlike most existing inferential methods which are tailored for individual models, our approach provides a general framework for high dimensional sparse inference (also called post-regularization inference).

Selected Paper No.5:

High Dimensional Semiparametric Gaussian Copula Graphical Models

Han Liu, Fang Han, Ming Yuan, John Lafferty, and Larry Wasserman

The Annals of Statistics, Volume 40, Number 40 (2012), pp2293-2326.

Blurb. This paper proposes a regularized rank-based estimator (e.g., based on the Kendall's tau correlation coefficients) to fit the nonGaussian graphical model. We prove that the proposed procedure simultaneously achieves the optimal parametric rates of convergence for both graph recovery and parameter estimation.

» Back to the top

Reading Group

We have weekly reading group. The topics of this quarter include large language models, multivariate time series forecasting, generative AI.

Get In Touch

Department of Computer Science
Mudd Hall 3119
Northwestern University
Evanston, IL 60201
Phone: +847 491 2793
Email: hanliu@northwestern.edu