Statistical Machine Learning Lab

Princeton University

Publications

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

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

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

    On the arXiv:1401.3940. 2014.

    Smooth Projected Density Dstimation

    Heather Battey and Han Liu

    On the arXiv:1308.3968. 2013.

    CODA: Copula Discriminant Analysis

    Fang Han, Tuo Zhao, and Han Liu

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

    Sparse Nonparametric Graphical Models

    John Lafferty, Han Liu, and Larry Wasserman

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

    Exponential Concentration Inequality for Mutual Information Estimation

    Han Liu, John Lafferty and Larry Wasserman

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

    Sparse Additive Machine

    Tuo Zhao and Han Liu

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

    Graph-Valued Regression

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

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

    Nonparametric Greedy Algorithm for the Sparse Learning Problems

    Han Liu and Xi Chen

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

    Sparse Additive Models

    Pradeep Ravikumar, John Lafferty, Han Liu, Larry Wasserman

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

    Nonparametric Regression and Classification with Joint Sparsity Constraints

    Han Liu, John Lafferty, and Larry Wasserman

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

    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.

    » Complete publication list

    Semiparametric Structural Sparsity

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

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

    On the arXiv:1401.3940. 2014.

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

    Fang Han and Han Liu

    Journal of American Statistical Association (Theory and Methodology) (JASA). To appear. 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.

    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.

    Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model

    Fang Han, Han Liu, and Brian Caffo

    On the arXiv:1310.3223. 2013.

    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) (JRSSB). To appear. 2013.

    CODA: Copula Discriminant Analysis

    Fang Han, Tuo Zhao, and Han Liu

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

    High Dimensional Semiparametric Bigraphical Model

    Yang Ning and Han Liu

    Biometrika. To appear. 2013.

    High Dimensional Semiparametric Scale-invariant Principal Component Analysis

    Fang Han and Han Liu

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

    Robust Sparse Principal Component Regression

    Fang Han and Han Liu

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

    Sparse Inverse Covariance Estimation with Calibration

    Tuo Zhao and Han Liu

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

    Principal Componenet Analysis on non-Gaussian Dependent Data

    Fang Han and Han Liu

    International Conference on Machine Learning (ICML). To appear. 2013. (Winner of the 2013 ENAR Distinguished Student Paper Award)

    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)

    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.

    The Nonparanormal SKEPTIC

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

    International Conference on Machine Learning (ICML), 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.

    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.

    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.

    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.

    » Complete publication list

    Model based Optimization

    A Strictly Contractive Peaceman-Rachford Splitting Method for Convex Programming

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

    SIAM Journal on Optimization. In press. 2014.

    Optimal Tests of Treatment Effects Using Sparse Linear Programming

    Michael Rosenblum, Han Liu, and En-Hsu Yen

    Journal of American Statistical Association (Theory and Methodology) (JASA). In press. 2014.

    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.

    High Dimensional Semiparametric Bigraphical Model

    Yang Ning and Han Liu

    Biometrika. To appear. 2013.

    Sparse Covariance Matrix Estimation with Eigenvalue Constraints

    Han Liu, Lie Wang, and Tuo Zhao

    Journal of Computational and Graphical Statistics (JCGS). To appear. 2013.

    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 (JCGS). To appear. 2013.

    Nonparanormal Graph Estimation via Smooth-projected Neighborhood Pursuit

    Tuo Zhao, Kathryn Roeder and Han Liu

    Neural Information Processing Systems (NIPS), 25, 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.

    Sparse Additive Machine

    Tuo Zhao and Han Liu

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

    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)

    Nonparametric Regression and Classification with Joint Sparsity Constraints

    Han Liu, John Lafferty, and Larry Wasserman

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

    » Complete publication list

    Large-Scale Calibrated Inference

    Multivariate Regression with Calibration

    Han Liu, Lie Wang, and Tuo Zhao

    On the arXiv:1305.2238. 2013.

    TIGER: A Tuning-Insensitive Approach for Optimal Graph Estimation

    Han Liu and Lie Wang

    On the arXiv:1209.2437. 2012.

    » Complete publication list

    High Dimensional Inference

    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. 2013.

    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.

    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.

    Optimal Feature Selection in High-Dimensional Discriminant Analysis

    Mladen Kolar and Han Liu

    On the arXiv:1306.6557. 2013.

    Graph Estimation From Multi-attribute Data

    Mladen Kolar, Han Liu, and Eric Xing

    Journal of Machine Learning Research (JMLR), In press. 2014.

    TIGER: A Tuning-Insensitive Approach for Optimal Graph Estimation

    Han Liu and Lie Wang

    On the arXiv:1209.2437. 2012.

    Robust Sparse Principal Component Regression

    Fang Han and Han Liu

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

    Sparse Inverse Covariance Estimation with Calibration

    Tuo Zhao and Han Liu

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

    Compressive Network Analysis

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

    IEEE Transactions on Automatic Control. To appear. 2013.

    Statistical Analysis of Big Data on Pharmacogenomics

    Jianqing Fan and Han Liu

    Advanced Drug Delivery Reviews. To appear. 2013.

    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) (JRSSB). To appear. 2013.

    CODA: Copula Discriminant Analysis

    Fang Han, Tuo Zhao, and Han Liu

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

    High Dimensional Semiparametric Bigraphical Model

    Yang Ning and Han Liu

    Biometrika. To appear. 2013.

    Sparse Covariance Matrix Estimation with Eigenvalue Constraints

    Han Liu, Lie Wang, and Tuo Zhao

    Journal of Computational and Graphical Statistics (JCGS). To appear. 2013.

    Sparse Principal Component Analysis for High Dimensional Multivariate Time Series

    Zhaoran Wang, Fang Han, and Han Liu

    Journal of Machine Learning Research (AISTATS track). To appear. 2013 (Winner of the Notable Paper Award)

    Transition Matrix Estimation in High Dimensional Vector Autoregressive Models

    Fang Han and Han Liu

    International Conference on Machine Learning (ICML). To appear. 2013.

    Principal Componenet Analysis on non-Gaussian Dependent Data

    Fang Han and Han Liu

    International Conference on Machine Learning (ICML). To appear. 2013. (Winner of the 2013 ENAR Distinguished Student Paper Award)

    Feature Selection in High-Dimensional Classification

    Mladen Kolar and Han Liu

    International Conference on Machine Learning (ICML). To appear. 2013.

    Graph Estimation From Multi-attribute Data

    Mladen Kolar, Han Liu and Eric Xing

    International Conference on Machine Learning (ICML). To appear. 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)

    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.

    The Nonparanormal SKEPTIC

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

    International Conference on Machine Learning (ICML), 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.

    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.

    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.

    Nonparametric Greedy Algorithm for the Sparse Learning Problems

    Han Liu and Xi Chen

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

    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)

    Sparse Additive Models

    Pradeep Ravikumar, John Lafferty, Han Liu, Larry Wasserman

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

    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.

    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.

    » Complete publication list

    Scientific Data Analysis

    Challenges of Big Data Analysis

    Jianqing Fan, Fang Han, and Han Liu

    National Science Review (NSR). To appear, 2013.

    Optimal Tests of Treatment Effects Using Sparse Linear Programming

    Michael Rosenblum, Han Liu, and En-Hsu Yen

    On the arXiv:1306.0964. 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.

    Graph Estimation From Multi-attribute Data

    Mladen Kolar, Han Liu, and Eric Xing

    On the arXiv:1210.7665. 2012.

    Statistical Analysis of Big Data on Pharmacogenomics

    Jianqing Fan and Han Liu

    Advanced Drug Delivery Reviews. To appear. 2013.

    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.

    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.

    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.

    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)

    Neural Semantic Basis Discovery using Simultaneous Sparse Approximation

    Mark Palatucci, Tom Mitchell, and Han Liu

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

    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.

    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.

    A Realistic Method for Real-time Obstacle Avoidance

    Han Liu and Yongji Wang

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

    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. pp173-187 Volume 22 Issue 2, 2004.

    » Complete publication list

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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