Statistical Machine Learning Lab

Princeton University

Publications

Journal Articles

Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model

Fang Han, Xiaoyan Han, Han Liu, and Brian Caffo

Tha Annals of Applied Statistics, To appear, 2015.

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

Yang Ning and Han Liu

Tha Annals of Statistics. To appear, 2015.

A Partially Linear Framework for Massive Heterogeneous Data

Tianqi Zhao, Guang Cheng, and Han Liu

Tha Annals of Statistics. To appear, 2015.

QUADRO: A Supervised Dimension Reduction Method via Rayleigh Quotient Optimization

Jianqing Fan, Tracy Ke, Han Liu, and Lucy Xia

Tha Annals of Statistics. Volume 43 (4), pp1498-1534, 2015.

Joint Estimation of Multiple Graphical Models from High Dimensional Time Series

Huitong Qiu, Fang Han, Han Liu, and Brian Caffo

Journal of the Royal Statistical Society: Series B (Statistical Methodology), To appear, 2015.

High Dimensional Semiparametric Latent Graphical Model for Mixed Data

Jianqing Fan, Han Liu, Yang Ning and Hui Zou

Journal of the Royal Statistical Society: Series B (Statistical Methodology), To appear, 2015.

Provable Sparse Tensor Decomposition

Wei Sun, Junwei Lu, Han Liu, Guang Cheng

Journal of the Royal Statistical Society: Series B (Statistical Methodology), To appear, 2015.

Discussion on ‘Sequential Quasi-Monte-Carlo Sampling’

Han Liu and Yong Zeng

Journal of the Royal Statistical Society: Series B (Statistical Methodology), To appear, 2015.

Discussion on ‘Analysis of Forensic DNA Mixtures with Artefacts’

Han Liu

Journal of the Royal Statistical Society: Series C (Applied Statistics), To appear, 2015.

Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions

Mengdi Wang, Ethan X. Fang and Han Liu

Mathematical Programming, Series A. To appear, 2015.

Generalized Alternating Direction Method of Multipliers: New Theoretical Insight and Application

Ethan X. Fang, Bingsheng He, Han Liu, Xiaoming Yuan

Mathematical Programming Computation, To appear, 2015.

Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification

Robert Vanderbei, Han Liu, Lie Wang, Kevin Lin

Mathematical Programming Computation, To appear, 2015.

Robust Inference of Risks of Large Portfolios

Jianqing Fan, Fang Han, Han Liu, Byron Vickers

Journal of Econometrics, To appear, 2015.

An Overview on the Estimation of Large Covariance and Precision Matrices

Jianqing Fan, Yuan Liao, Han Liu

The Econometrics Journal, To appear, 2015.

A Semiparametric Graphical Modelling Approach for Large-scale Equity Selection

Han Liu, John Mulvey, Tianqi Zhao

Quantitative Finance, To appear, 2015.

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

Haochang Shou, Russell T. Shinohara, Han Liu, Daniel S. Reich, Ciprian M. Crainiceanu

Journal of Computational and Graphical Statistics, In press, 2015.

Multivariate Regression with Calibration

Han Liu, Lie Wang, and Tuo Zhao

Journal of Machine Learning Research, In press, 2015.

Optimal Computational and Statistical Rates of Convergence for Sparse Nonconvex Learning Problems

Zhaoran Wang, Han Liu, and Tong Zhang

The Annals of Statistics. Volume 42(6), pp2164-2201, 2014.

Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming

Michael Rosenblum, Han Liu, and En-Hsu Yen

Journal of American Statistical Association (Theory and Methodology). Volume 109(507), pp1216-1228. 2014.

Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data

Fang Han and Han Liu

Journal of American Statistical Association (Theory and Methodology), Volume 109(505), pp275-287. 2014.

A Strictly Contractive Peaceman-Rachford Splitting Method for Convex Programming

Bingsheng He, Han Liu, Zhaoran Wang, and Xiaoming Yuan

SIAM Journal on Optimization, Volume 24(3), pp1011-1040. 2014.

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning

Le Song, Han Liu, Ankur Parikh, and Eric Xing

Journal of Machine Learning Research. In Press. 2014.

Graph Estimation From Multi-attribute Data

Mladen Kolar, Han Liu, and Eric Xing

Journal of Machine Learning Research,Volume 15(1), pp1713-1750. 2014.

The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation

Haotian Pang, Han Liu, and Robert Vanderbei

Journal of Machine Learning Research, Volume 15, pp489-493. 2014.

An R Package flare for High Dimensional Linear Regression and Precision Matrix Estimation

Xingguo Li, Tuo Zhao, Xiaoming Yuan, and Han Liu

Journal of Machine Learning Research, In press. 2014.

Sparse Covariance Matrix Estimation with Eigenvalue Constraints

Han Liu, Lie Wang, and Tuo Zhao

Journal of Computational and Graphical Statistics, Volume 23(2), pp439-459, 2014.

Positive Semidefinite Rank-based Correlation Matrix Estimation with Application to Semiparametric Graph Estimation

Tuo Zhao, Kathryn Roeder, and Han Liu

Journal of Computational and Graphical Statistics. In press. 2014.

Discussion on ‘Large covariance estimation by thresholding principal orthogonal complements’

Han Liu and Lie Wang

Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 75 pp668. 2014.

Discussion on ‘Multiscale Change-Point Inference’

Han Liu

Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB). In press. 2014.

Challenges of Big Data Analysis

Jianqing Fan, Fang Han, and Han Liu

National Science Review, Volume 2(1), pp1-24, 2014.

Optimal Feature Selection in High-Dimensional Discriminant Analysis

Mladen Kolar and Han Liu

IEEE Transactions on Information Theory. In press. 2014.

Calibrated Precision Matrix Estimation for High Dimensional Elliptical Distributions

Tuo Zhao and Han Liu

IEEE Transactions on Information Theory. In press. 2014.

Compressive Network Analysis

Xiaoye Jiang, Yuan Yao, Han Liu, and Leonidas Guibas

IEEE Transactions on Automatic Control. In press. 2014.

High Dimensional Semiparametric Scale-invariant Principal Component Analysis

Fang Han and Han Liu

IEEE Transactions on Pattern Analysis and Machine Intelligence. In press. 2014.

High Dimensional Semiparametric Bigraphical Model

Yang Ning and Han Liu

Biometrika 100 (3): 655-670.. 2013.

CODA: Copula Discriminant Analysis

Fang Han, Tuo Zhao, and Han Liu

Journal of Machine Learning Research (JMLR). Volume 14, pp629-671. 2013.

Statistical Analysis of Big Data on Pharmacogenomics

Jianqing Fan and Han Liu

Advanced Drug Delivery Reviews. Volume 65, Issue 7, pp987-1000. 2013.

High Dimensional Semiparametric Gaussian Copula Graphical Models

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

The Annals of Statistics, Volume 40, No. 40, pp2293-2326. 2012. (Winner of the David P. Byar Young Investigator Travel Award sponsered by ASA Biometrics section)

Sparse Nonparametric Graphical Models

John Lafferty, Han Liu, and Larry Wasserman

Statistical Science, Volume 27, No 4, pp519-537. 2012.

An Efficient Optimization Algorithm for Structured Sparse CCA, with Applications to eQTL Mapping

Xi Chen and Han Liu

Statistics in Biosciences, Vol 4, No 1, pp3-26. 2012.

HUGE: High Dimensional Undirected Graph Estimation

Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, and Larry Wasserman

Journal of Machine Learning Research (JMLR), Vol 3, pp1059-1062. 2012.

Forest Density Estimation

Han Liu, Min Xu, Haijie Gu, Anupam Dasgupta, John Lafferty, and Larry Wasserman

Journal of Machine Learning Research (JMLR) Vol 12, 907−951. 2011.

The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

Han Liu, John Lafferty, and Larry Wasserman

Journal of Machine Learning Research (JMLR), (10) 2295-2328, 2009.

Mining Past Query Trails to Label Long and Rare Search Engine Queries

Peter Bailey, Ryen W. White, Han Liu, and Giridhar Kumaran

ACM Transactions on the Web (ACM TWEB) 1(2) 1-25, 2010.

Sparse Additive Models

Pradeep Ravikumar, John Lafferty, Han Liu, Larry Wasserman

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2009.

A Framework for Efficient Association Rule Mining in XML Data

Ji Zhang, Han Liu, Tok Wang Ling, Robert M.Bruckner, and A. Min Tjoa

Journal of Database Management, Volume 17(3), pp19-40, 2006.

A Real-time Robot Path Planning Approach without the Computation of Cspace Obstacles

Yongji Wang, Han Liu, Qing Wang, Mingshu Li, Jinhui Zhou and M. Cartmell

Journal of Robotica, Volume 22(2), pp173-187, 2004.

Preprints

The Knowledge Gradient Policy Using A Sparse Additive Belief Model

Yan Li, Han Liu, Warren Powell

On the arXiv:1503.05567. 2015.

Post-Regularization Confidence Bands for High Dimensional Nonparametric Models with Local Sparsity

Junwei Lu, Mladen Kolar, Han Liu

On the arXiv:1503.02978. 2015.

Statistical Limits of Convex Relaxations

Zhaoran Wang, Quanquan Gu, Han Liu

On the arXiv:1501.02382. 2015.

An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices

Cheng Zhou, Fang Han, Xinsheng Zhang, Han Liu

On the arXiv:1503.01442. 2015.

Local and Global Inference for High Dimensional Gaussian Copula Graphical Models

Quanquan Gu, Yuan Cao, Yang Ning, Han Liu

On the arXiv:1502.02347. 2015.

High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality

Zhaoran Wang, Quanquan Gu, Yang Ning, and Han Liu

On the arXiv:1412.8729. 2014.

A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression

Tianqi Zhao, Mladen Kolar, and Han Liu

On the arXiv:1412.8724. 2014.

On Semiparametric Exponential Family Graphical Models

Zhuoran Yang, Yang Ning, and Han Liu

On the arXiv:1412.8697. 2014.

SPARC: Optimal Estimation and Asymptotic Inference under Semiparametric Sparsity

Yang Ning and Han Liu

On the arXiv:1412.2295. 2014.

Testing and Confidence Intervals for High Dimensional Proportional Hazards Model

Ethan X. Fang, Yang Ning and Han Liu

On the arXiv:1412.5158. 2014.

A General Theory of Pathwise Coordinate Optimization

Tuo Zhao, Han Liu, and Tong Zhang

On the arXiv:1412.7477. 2014.

Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time

Zhaoran Wang, Huanran Lu and Han Liu

On the arXiv:1408.5352. 2014.

On the Impact of Dimension Reduction on Graphical Structures

Fang Han, Huitong Qiu, Han Liu and Brian Caffo

On the arXiv:1404.7547. 2014.

Transition Matrix Estimation in High Dimensional Vector Autoregressive Models

Fang Han and Han Liu

On the arXiv:1307.0293. 2013.

Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models

Zhaoran Wang, Fang Han, and Han Liu

On the arXiv:1307.0164. 2013.

Smooth Projected Density Estimation

Heather Battey and Han Liu

On the arXiv:1308.3968. 2013.

TIGER: A Tuning-Insensitive Approach for Optimal Graph Estimation

Han Liu and Lie Wang

On the arXiv:1209.2437. 2012.

Conference Proceedings

Multivariate Regression with Calibration

Han Liu, Lie Wang and Tuo Zhan

Neural Information Processing Systems (NIPS), 27, 2014.

Mode Estimation for High Dimensional Discrete Tree Graphical Models

Chao Chen, Han Liu, Dimitris Metaxas and Tianqi Zhao

Neural Information Processing Systems (NIPS), 27, 2014.

Oracle Sparse PCA and Its Inference

Quanquan Gu, Zhaoran Wang and Han Liu

Neural Information Processing Systems (NIPS), 27, 2014.

Accelerated Mini-batch Randomized Block Coordinate Descent Method

Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora and Han Liu

Neural Information Processing Systems (NIPS), 27, 2014.

Tightening after Relax: Minimax-Optimal Sparse PCA in Polynomial Time

Zhaoran Wang and Han Liu

Neural Information Processing Systems (NIPS), 27, 2014.

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series

Zhaoran Wang, Fang Han, and Han Liu

Journal of Machine Learning Research (AISTATS Track). WCP 31 : pp48–56 2013 (Winner of the Notable Paper Award)

Robust Sparse Principal Component Regression

Fang Han and Han Liu

Neural Information Processing Systems (NIPS), 26. 2013.

Sparse Inverse Covariance Estimation with Calibration

Tuo Zhao and Han Liu

Neural Information Processing Systems (NIPS), 26.2013.

Transition Matrix Estimation in High Dimensional Vector Autoregressive Models

Fang Han and Han Liu

Journal of Machine Learning Research (ICML Track). WCP 28 (1): pp172-180. 2013.

Principal Componenet Analysis on non-Gaussian Dependent Data

Fang Han and Han Liu

Journal of Machine Learning Research (ICML Track). WCP 28 (1): pp240-248. 2013. (Winner of the 2013 ENAR Distinguished Student Paper Award)

Feature Selection in High-Dimensional Classification

Mladen Kolar and Han Liu

Journal of Machine Learning Research (ICML Track). WCP 28 (1): pp329-337. 2013.

Graph Estimation From Multi-attribute Data

Mladen Kolar, Han Liu and Eric Xing

Journal of Machine Learning Research (ICML Track). WCP 28 (3): pp73-81.2013.

Exponential Concentration Inequality for Mutual Information Estimation

Han Liu, John Lafferty and Larry Wasserman

Neural Information Processing Systems (NIPS), 25, 2012.

High Dimensional Transelliptical Graphical Models

Han Liu, Fang Han and Cun-hui Zhang

Neural Information Processing Systems (NIPS), 25, 2012.

Transelliptical Principal Component Analysis for non-Gaussian Data

Fang Han and Han Liu

Neural Information Processing Systems (NIPS), 25, 2012.

Transelliptical Component Analysis

Fang Han and Han Liu

Neural Information Processing Systems (NIPS), 25, 2012.

Nonparanormal Graph Estimation via Smooth-projected Neighborhood Pursuit

Tuo Zhao, Kathryn Roeder and Han Liu

Neural Information Processing Systems (NIPS), 25, 2012.

Marginal Regression For Multitask Learning

Mladen Kolar and Han Liu

Journal of Machine Learning Research (AISTATS track), WCP Vol 22, pp647-655, 2012.

Sparse Additive Machine

Tuo Zhao and Han Liu

Journal of Machine Learning Research (AISTATS track), WCP Vol 22, pp1435-1443, 2012.

Structured Sparse Canonical Correlation Analysis

Xi Chen and Han Liu

Journal of Machine Learning Research (AISTATS track), WCP Vol 22, pp199-207, 2012.

Network Clique Detection using Radon Basis Pursuit

Xiaoye Jiang, Yuan Yao, Han Liu, and Leo Guibas

Journal of Machine Learning Research (AISTATS track), WCP Vol 22, pp565-573, 2012.

The Nonparanormal SKEPTIC

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

International Conference on Machine Learning (ICML), 2012.

Stability Approach to Regularization Selection (StARS) for High-Dim Graphical Models

Han Liu, Kathryn Roeder and Larry Wasserman

Neural Information Processing Systems (NIPS), 23, 2010.

Graph-Valued Regression

Han Liu, Xi Chen, John Lafferty, and Larry Wasserman

Neural Information Processing Systems (NIPS), 23, 2010.

The Group Dantzig Selector

Han Liu, Jian Zhang, Xiaoye Jiang, and Jun Liu

Journal of Machine Learning Research (JMLR), WCP Volume 9, pp461-468, 2010.

Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis

Xi Chen, Yan Liu, Han Liu and Jaime G. Carbonell

The Twenty-Fourth Conference on Artificial Intelligence (AAAI), 2010.

Nonparametric Greedy Algorithm for the Sparse Learning Problems

Han Liu and Xi Chen

Neural Information Processing Systems (NIPS), 22, 2009.

Blockwise Coordinate Descent Procedures for the Multi-task Lasso

Han Liu, Mark Palatucci, and Jian Zhang

The Twenty-sixth International Conference on Machine Learning (ICML), 2009. (Winner of the Best Student Paper Award and Best Overall Paper Honorable Mention)

On the Estimation Consistency of the Group Lasso and its Applications

Han Liu and Jian Zhang

Journal of Machine Learning Research (AISTATS track), WCP-Volume 5:pp376-383, 2009. (Best Paper Award Nominee at AISTATS's 09)

On the Estimation and Variable Selection Consistency of the Bock q-norm Regression

Han Liu and Jian Zhang

Technical Report, Department of Statistics, Carnegie Mellon University (CMU-STAT-TR-86), 2009.

Some Two-step Procedures for Variable Selection in High-dimensional Linear Regression

Jian Zhang, Jessie Jeng, and Han Liu

Technical Report, Department of Statistics, Purdue University (Purdue-STAT-TR-08-05), 2009.

Nonparametric Regression and Classification with Joint Sparsity Constraints

Han Liu, John Lafferty, and Larry Wasserman

Neural Information Processing Systems (NIPS), 21, 2008.

Neural Semantic Basis Discovery using Simultaneous Sparse Approximation

Mark Palatucci, Tom Mitchell, and Han Liu

Sparse Optimization and Variable Selection Workshop (ICML), 2008.

SpAM: Sparse Additive Models

Pradeep Ravikumar, Han Liu, John Lafferty and Larry Wasserman

Neural Information Processing Systems (NIPS), 20, 2007.

Sparse Nonparametric Density Estimation in High Dimensions using the Rodeo

Han Liu, John Lafferty, and Larry Wasserman

Journal of Machine Learning Research (AISTATS track), WCP-Volume 2:283-290, 2007.

Towards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data

Anthony Bonner and Han Liu

The Sixth SIAM International Conference on Data Mining (SDM), 2006.

Canonical Correlation, an Approximation, and the Prediction of Protein Abundance

Anthony Bonner and Han Liu

The Eighth Workshop on Mining Scientific and Engineering Datasets (MSD), 2005.

A Generalized Real-Time Obstacle Avoidance Method Without the Cspace Calculation

Yong-Ji Wang, Matthew Cartmell, Qiu-Ming Tao, Han Liu

Journal of Computer Science and Technology (JCST), Volume 20(6): 774-787, 2005.

D-GridMST: Clustering Large Distributed Spatial Databases

Ji Zhang and Han Liu

Book Chapter of Classification and Clustering for Knowledge Discovery. Springer-Verlag Publisher, ISBN 978-3-540-26073-8, 2005.

Dimension Reduction with Penalized Independent Component Analysis

Han Liu and Rafal Kustra

The NIPS workshop on New Problems and Methods in Computational Biology (NIPS), 2005.

Predicting Protein Levels from Tandem Mass Spectrometry Data

Anthony Bonner and Han Liu

The NIPS workshop on New Problems and Methods in Computational Biology (NIPS), 2005.

Comparison of Discrimination Methods for Peptide Classification in MS/MS

Anthony Bonner and Han Liu

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB), 2004.

Modeling Protein MS/MS Data with an Extended Linear Regression Strategy

Han Liu, Anthony Bonner, and Andrew Emily

The Twenty-sixth Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC), 2004.

An Efficient Method to Estimate Labeled Sample Size for Transductive LDA(QDA/MDA)

Han Liu, Xiaobin Yuan, Qianying Tang, and Rafal Kustra

The Fifteenth European Conference on Machine Learning (ECML), 2004

A Realistic Method for Real-time Obstacle Avoidance

Han Liu and Yongji Wang

IEEE International Conference on Robotics and Automaton (IEEE RAM), 2004.

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Reading Group

We have biweekly reading group. The topics of this semeser include optimization in infinite dimensional space, homotopy algorithm, stochastic convex optimization, random matrix theory, and CUDA for GPU programming.

Get In Touch

Department of Operations Research and Financial Engineering
Sherred Hall 224
Princeton University, NJ 08544
Phone: +609 258 1788
Email: hanliu@princeton.edu