Publications

  •        Nonparametric Functional Sparsity
  •        Semiparametric Structural Sparsity
  •        Model based Optimization
  •        Large-Scale Calibrated Inference
  •        Theoretical Foundations of High Dimensional Inference
  •        Modern Scientific Applications
  • Preprints

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

    Zhaoran Wang, Huanran Lu and Han Liu

    On the arXiv:1408.5352. 2014.

    High Dimensional Semiparametric Latent Graphical Model for Mixed Data

    Jianqing Fan, Han Liu, Yang Ning and Hui Zou

    On the arXiv:1404.7236. 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.

    Optimal Feature Selection in High-Dimensional Discriminant Analysis

    Mladen Kolar and Han Liu

    On the arXiv:1306.6557. 2013.

    QUADRO: A Supervised Dimension Reduction Method via Rayleigh Quotient Optimization

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

    On the arXiv:1311.5542. 2013.

    Joint Estimation of Multiple Graphical Models from High Dimensional Time Series

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

    On the arXiv:1311.0219, 2013.

    Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model

    Fang Han, Han Liu, and Brian Caffo

    On the arXiv:1310.3223. 2013.

    Transition Matrix Estimation in High Dimensional Vector Autoregressive Models

    Fang Han and Han Liu

    On the arXiv:1307.0293. 2013.

    Multivariate Regression with Calibration

    Han Liu, Lie Wang, and Tuo Zhao

    On the arXiv:1305.2238. 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.

    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

    On the arXiv:1306.5524. 2013.

    TIGER: A Tuning-Insensitive Approach for Optimal Graph Estimation

    Han Liu and Lie Wang

    On the arXiv:1209.2437. 2012.

    Journal Articles

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

    Zhaoran Wang, Han Liu, and Tong Zhang

    The Annals of Statistics. In press. 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). In press. 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.

    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.

    Conference Proceedings

    Multivariate Regression with Calibration

    Han Liu, Lie Wang and Tuo Zhan

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

    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