Selected Journal Papers (Statistical Theory and Methodology)
- Tianxi Li, Cheng Qian, Elizaveta Levina, and Ji Zhu (2020+) High-dimensional Gaussian graphical models on network-linked data. Journal of Machine Learning Research. Accepted. [PDF][CODE]
- Tianxi Li, Elizaveta Levina, and Ji Zhu (2020+) Network cross-validation by edge sampling. Biometrika. Accepted. [PDF][CODE] (One of three winning papers in the 2017 ASA Student Paper Competition sponsored by the Nonparametric Statistics Section)
- Jianwei Hu, Jingfei Zhang, Hong Qin, Ting Yan, and Ji Zhu (2020+) Using maximum entry-wise deviation to test the goodness-of-fit for stochastic block models. Journal of the American Statistical Association. Accepted. [PDF][CODE]
- Tianxi Li, Elizaveta Levina, and Ji Zhu (2019+) Prediction models for network-linked data. Annals of Applied Statistics. Accepted. [PDF][CODE] (One of two winning papers in the 2015 ASA Student Paper Competition sponsored by the SSPA Section)
- Zhe Fei, Ji Zhu, Moulinath Banerjee, and Yi Li (2019+) Drawing inferences for high-dimensional linear models: a selection-assisted partial regression and smoothing approach. Biometrics. Accepted. [PDF][CODE]
- Yunpeng Zhao, Yun-Jhong Wu, Elizaveta Levina, and Ji Zhu (2017) Link prediction for partially observed networks. Journal of Computational and Graphical Statistics 26(3):725-733. [PDF][CODE]
- Yuan Zhang, Elizaveta Levina, and Ji Zhu (2017) Estimating network edge probabilities by neighborhood smoothing. Biometrika 104(4):771-783. [PDF][CODE] (Winner of the 2016 ASA Student Paper Competition sponsored by the Nonparametric Statistics Section)
- Kevin He, Yuan Yang, Yanming Li, Ji Zhu, and Yi Li (2017) Modeling time-varying effects with large-scale survival data: an efficient quasi-Newton approach. Journal of Computational and Graphical Statistics 26(3):635-645. [PDF][CODE]
- Jie Cheng, Tianxi Li, Elizaveta Levina, and Ji Zhu (2017) High-dimensional mixed graphical models. Journal of Computational and Graphical Statistics 26(2):367-378. [PDF][CODE]
- Ting Yan, Chenlei Leng, and Ji Zhu (2016) Asymptotics in directed exponential random graph models with an increasing bi-degree sequence. Annals of Statistics 44(1):31-57. [PDF]
- Kevin He, Yanming Li, Ji Zhu, Hongliang Liu, Jeffrey Lee, Christopher Amos, Terry Hyslop, Jiashun Jin, Huazhen Lin, Qinyi Wei, and Yi Li (2016) Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates. Bioinformatics 32(1):50-57. [PDF]
- Na Zou, Yun Zhu, Ji Zhu, Mustafa Baydogan, Wei Wang, and Jing Li (2015) A transfer learning approach for predictive modeling of degenerate biological systems. Technometrics 57(3):362-373. [PDF]
- Peirong Xu, Ji Zhu, Lixing Zhu, and Yi Li (2015) Covariance-enhanced discriminant analysis. Biometrika 102(1):33-45. [PDF]
- Ashin Mukherjee, Kun Chen, Naisyin Wang, and Ji Zhu (2015) On the degrees of freedom of reduced-rank estimators in multivariate regression. Biometrika 102(2):457-477. [PDF][CODE]
- Yun Li, Ji Zhu, and Naisyin Wang (2015) Regularized semiparametric estimation for ordinary differential equations. Technometrics 57(3):341-350. [PDF][CODE]
- Yanming Li, Bin Nan, and Ji Zhu (2015) Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure. Biometrics 71(2):354-363. [PDF][CODE]
- Jian Guo, Elizaveta Levina, George Michailidis, and Ji Zhu (2015) Graphical models for ordinal data. Journal of Computational and Graphical Statistics 24(1):183-204. [PDF][CODE]
- Jian Guo, Jie Cheng, Elizaveta Levina, George Michailidis, and Ji Zhu (2015) Estimating heterogeneous graphical models for discrete data with an application to roll call voting. Annals of Applied Statistics 9(2):821-848. [PDF][CODE]
- Peter Bickel, Aiyou Chen, Yunpeng Zhao, Elizaveta Levina, and Ji Zhu (2015) Correction to the proof of consistency of community detection. Annals of Statistics 43:462-466. [PDF]
- Xuejing Wang, Bin Nan, Ji Zhu, and Robert Koeppe (2014) Regularized 3D functional regression for brain imaging via Haar wavelets. Annals of Applied Statistics 8:1045-1064. [PDF]
- Jie Cheng, Elizaveta Levina, Pei Wang, and Ji Zhu (2014) A sparse Ising model with covariates. Biometrics 70(4):943-953. [PDF][CODE]
- Yunpeng Zhao, Elizaveta Levina, and Ji Zhu (2012) Consistency of community detection in networks under degree-corrected stochastic block models. Annals of Statistics 40(4):2266-2292. [PDF]
- Yunpeng Zhao, Elizaveta Levina, and Ji Zhu (2011) Community extraction for social networks. Proceedings of the National Academy of Sciences 108(18):7321-7326. [PDF] (One of four winning papers in the 2011 ASA Student Paper Competition sponsored by the Statistical Computing Section)
- Sijian Wang, Bin Nan, Saharon Rosset, and Ji Zhu (2011) Random lasso. Annals of Applied Statistics 5(1):468-485. [PDF][CODE]
- Jian Guo, Elizaveta Levina, George Michailidis, and Ji Zhu (2011) Joint estimation of multiple graphical models. Biometrika 98(1):1-15. [PDF][CODE] (One of five winning papers in the 2010 ASA Student Paper Competition sponsored by the Statistical Learning and Data Mining Section)
- Adam Rothman, Elizaveta Levina, and Ji Zhu (2010) Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics 19(4):947-962. [PDF]
- Adam Rothman, Elizaveta Levina, and Ji Zhu (2010) A new approach to Cholesky-based covariance regularization in high dimensions. Biometrika 97(3):539-550. [PDF]
- Jie Peng, Ji Zhu, Anna Bergamaschi, Wonshik Han, Dong-Young Noh, Jonathan Pollack, and Pei Wang (2010) Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. Annals of Applied Statistics 4(1):53-77. [PDF][CODE]
- Gareth James, Chiara Sabatti, Nengfeng Zhou, and Ji Zhu (2010) Sparse regulation networks. Annals of Applied Statistics 4(2):663-686. [PDF][CODE]
- Jian Guo, Elizaveta Levina, George Michailidis, and Ji Zhu (2010) Pairwise variable selection for high-dimensional model-based clustering. Biometrics 66(3):793-804. [PDF][CODE] (One of four winning papers in the 2009 ASA Student Paper Competition sponsored by the Statistical Computing Section)
- Jian Guo, Gareth James, Elizaveta Levina, George Michailidis, and Ji Zhu (2010) Principal component analysis with sparse fused loadings. Journal of Computational and Graphical Statistics 19(4):930-946. [PDF]
- Nam-Hee Choi, William Li, and Ji Zhu (2010) Variable selection with the strong heredity constraint and its oracle property. Journal of the American Statistical Association 105(489):354-364. [PDF][CODE] (One of the winning papers in the 2007 ENAR Student Paper Competition)
- Sijian Wang, Bin Nan, Nengfeng Zhou, and Ji Zhu (2009) Hierarchically penalized Cox regression for censored data with grouped variables and its oracle property. Biometrika 96(2):307-322. [PDF][CODE] (Winner of the 2008 ICSA J.P. Hsu Memorial Award)
- Adam Rothman, Elizaveta Levina, and Ji Zhu (2009) Generalized thresholding of large covariance matrices. Journal of the American Statistical Association 104(485):177-186. [PDF]
- Jie Peng, Pei Wang, Nengfeng Zhou, and Ji Zhu (2009) Partial correlation estimation by joint sparse regression models. Journal of the American Statistical Association 104(486):735-746. [PDF][CODE]
- Gareth James, Jing Wang, and Ji Zhu (2009) Functional linear regression that's interpretable. Annals of Statistics 37(5A):2083-2108. [PDF][CODE]
- Hui Zou, Ji Zhu, and Trevor Hastie (2008) New multi-category boosting algorithms based on multi-category Fisher-consistent losses. Annals of Applied Statistics 2(4):1290-1306. [PDF]
- Sijian Wang and Ji Zhu (2008) Variable selection for model-based high-dimensional clustering and its application to microarray data. Biometrics 64(2):440-448. [PDF][CODE]
- Sijian Wang, Bin Nan, Ji Zhu, and David Beer (2008) Doubly penalized Buckley-James method for survival data with high-dimensional covariates. Biometrics 64(1):132-140. [PDF][CODE] (Winner of the 2007 ENAR John van Ryzin Award)
- Li Wang, Ji Zhu, and Hui Zou (2008) Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics 24(3):412-419. [PDF][CODE]
- Youjuan Li and Ji Zhu (2008) L1-norm quantile regression. Journal of Computational and Graphical Statistics 17(1):163-185. [PDF][CODE] (One of four winning papers in the 2006 ASA Student Paper Competition sponsored by the Statistical Computing Section)
- Elizaveta Levina, Adam Rothman, and Ji Zhu (2008) Sparse estimation of large covariance matrices via a nested lasso penalty. Annals of Applied Statistics 2(1):245-263. [PDF]
- Sijian Wang and Ji Zhu (2007) Improved centroids estimation for the nearest shrunken centroid classifier. Bioinformatics 23(8):972-979. [PDF][CODE] (One of four winning papers in the 2007 ASA Student Paper Competition sponsored by the Statistical Computing Section)
- Saharon Rosset and Ji Zhu (2007) Piecewise linear regularized solution paths. Annals of Statistics 35(3):1012-1030. [PDF][CODE]
- Youjuan Li and Ji Zhu (2007) Analysis of array CGH data for cancer studies using the fused quantile regression. Bioinformatics 23(18):2470-2476. [PDF]
- Youjuan Li, Yufeng Liu, and Ji Zhu (2007) Quantile regression in reproducing kernel Hilbert spaces. Journal of the American Statistical Association 102(477):255-268. [PDF][CODE]
- Lacey Gunter and Ji Zhu (2007) Efficient computation and model selection for the support vector regression. Neural Computation 19(6):1633-1655. [PDF][CODE]
- Ji Zhu and Trevor Hastie (2005) Kernel logistic regression and the import vector machine. Journal of Computational and Graphical Statistics 14(1):185-205. [PDF][CODE] (One of four winning papers in the 2002 ASA Student Paper Competition sponsored by the Statistical Computing Section)
- Rob Tibshirani, Michael Saunders, Saharon Rosset, Ji Zhu and Keith Knight (2005) Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society, Series B 67(1):91-108. [PDF][CODE]
- Saharon Rosset, Ji Zhu, and Trevor Hastie (2004) Boosting as a regularized path to a maximum margin classifier. Journal of Machine Learning Research 5:941-973. [PDF]
- Trevor Hastie, Saharon Rosset, Rob Tibshirani, and Ji Zhu (2004) The entire regularization path for the support vector machine. Journal of Machine Learning Research 5:1391-1415. [PDF][CODE]
Selected Journal Papers (Interdisciplinary Research)
- Wenshuo Liu, Karandeep Singh, Andrew Ryan, Devraj Sukul, Elham Mahmoudi, Akbar Waljee, Cooper Stansbury, Ji Zhu, and Brahmajee Nallamouthu (2020+) Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding. PLoS ONE. Accepted.
- Akbar Waljee, Beth Wallace, Shirley Cohen-Mekelburg, Yumu Liu, Boang Liu, Kay Sauder, Ryan Stidham, Ji Zhu, and Peter Higgins (2019) Development and validation of machine learning models in prediction of remission in patients with moderate to severe crohn disease. Journal of the American Medical Association Network Open 2(5): e193721.
- Ryan Stidham, Wenshuo Liu, Shrinivas Bishu, Michael Rice, Peter Higgins, Ji Zhu, Brahmajee Nallamothu, and Akbar Waljee (2019) Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. Journal of the American Medical Association Network Open 2(5): e193963.
- Akbar Waljee, Kay Sauder, Yiwei Zhang, Ji Zhu, and Peter Higgins (2018) External validation of a thiopurine monitoring algorithm on the SONIC clinical trial dataset. Clinical Gastroenterology and Hepatology 16(3):449-451.
- Akbar Waljee, Boang Liu, Kay Sauder, Ji Zhu, Shail Govani, Ryan Stidham, and Peter Higgins (2018) Predicting corticosteroid-free endoscopic remission with vedolizumab in ulcerative colitis. Alimentary Pharmacology & Therapeutics 47(6):763-772.
- Akbar Waljee, Boang Liu, Kay Sauder, Ji Zhu, Shail Govani, Ryan Stidham, and Peter Higgins (2018) Predicting corticosteroid-free biologic remission with vedolizumab in Crohn's disease. Inflammatory Bowel Diseases 24(6):1185-1192.
- Akbar Waljee, Kay Sauder, Anand Patel, Sandeep Segar, Boang Liu, Yiwei Zhang, Ji Zhu, Ryan Stidham, Ulysses Balis, and Peter Higgins (2017) Machine learning algorithms for objective remission and clinical outcomes with thiopurines. Journal of Crohn's and Colitis 11(7):801-810.
- Akbar Waljee, Rachel Lipson, Wyndy Wiitala, Yiwei Zhang, Boang Liu, Ji Zhu, Beth Wallace, Shail Govani, Ryan Stidham, Rodney Hayward, and Peter Higgins (2017) Predicting hospitalization and outpatient corticosteroid use in inflammatory bowel disease patients using machine learning. Inflammatory Bowel Diseases 24(1):45-53.
- Sa Liang, Zhengling Qi, Shen Qu, Ji Zhu, Anthony Chiu, Xiaoping Jia, and Ming Xu (2016) Scaling of global input-output networks. Physica A 452:311-319.
- Hua Cai, Xiaowei Zhan, Ji Zhu, Xiaoping Jia, Anthony Chiu, and Ming Xu (2016) Understanding taxi travel patterns. Physica A 457:590-597.
- Monica Konerman, Yiwei Zhang, Ji Zhu, Peter Higgins, Anna Lok, and Akbar Waljee (2015) Improvement of predictive models of risk of disease progression in chronic hepatitis C by incorporating longitudinal data. Hepatology 61(6):1832-1841.
- Amit Singal, Ashin Mukherjee, B. Joseph Elmunzer, Peter Higgins, Anna Lok, Ji Zhu, Jorge Marrero, and Akbar Waljee (2013) Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma. American Journal of Gastroenterology 108(11):1723-1730.
- Ashish Sood, Gareth James, Gerard Tellis, and Ji Zhu (2012) Predicting the path of technology innovation: SAW versus Moore, Bass, Gompertz and Kryder. Marketing Science 31(6):964-979.
- Akbar Waljee, Joel Joyce, Sijian Wang, Aditi Saxena, Margaret Hart, Ji Zhu, and Peter Higgins (2010) Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines. Clinical Gastroenterology and Hepatology 8:143-150.
- Haijun Yang, Byron Roe, and Ji Zhu (2007) Studies of stability and robustness for artificial neural networks and boosted decision trees. Nuclear Instruments and Methods for Physics Research, Section A 574(2):342-349.
- Susan Murphy, David Oslin, John Rush, and Ji Zhu for MCATS (2007) Methodological challenges in constructing effective treatment sequences for chronic psychiatric disorders. Neuropsychopharmacology 32(2):257-262.
- Pete Ulintz, Ji Zhu, Zhaohui Qin, and Phil Andrews (2006) Improved classification of mass spectrometry database search results using newer machine learning approaches. Molecular and Cellular Proteomics 5(3):497-509.
- Haijun Yang, Byron Roe, and Ji Zhu (2005) Studies of boosted decision trees for MiniBooNE particle identification. Nuclear Instruments and Methods for Physics Research, Section A 555(1-2):370-385.
- Byron Roe, Haijun Yang, Ji Zhu, Yong Liu, Ion Stancu, and Gordon McGregor (2005) Boosted decision trees as an alternative to artificial neural networks for particle identification. Nuclear Instruments and Methods for Physics Research, Section A 543(2-3):577-584.
Selected Refereed Conference Papers
- Jiaqi Ma, Weijing Tang, Ji Zhu, and Qiaozhu Mei (2019) A flexible generative framework for graph-based semi-supervised learning. The Annual Conference on Neural Information Processing Systems 32. [PDF]
- Saharon Rosset, Nathan Srebro, Grzegorz Swirszcz, and Ji Zhu (2007) L1 regularization in infinite dimensional feature spaces. The Annual Conference on Learning Theory 20. [PDF]
- Lacey Gunter and Ji Zhu (2005) Computing the solution path for the regularized support vector regression. The Annual Conference on Neural Information Processing Systems 18. [PDF][CODE]
- Saharon Rosset, Ji Zhu, Hui Zou, and Trevor Hastie (2004) A method for inferring label sampling mechanisms in semi-supervised learning. The Annual Conference on Neural Information Processing Systems 17. [PDF]
- Trevor Hastie, Saharon Rosset, Rob Tibshirani, and Ji Zhu (2004) The entire regularization path for the support vector machine. The Annual Conference on Neural Information Processing Systems 17. [PDF][CODE] (One of the oral presentation papers at NIPS 2004)
- Ji Zhu, Saharon Rosset, Rob Tibshirani, and Trevor Hastie (2003) 1-norm support vector machines. The Annual Conference on Neural Information Processing Systems 16. [PDF][CODE] (One of the spotlight papers at NIPS 2003)
- Saharon Rosset, Ji Zhu, and Trevor Hastie (2003) Margin maximizing loss functions. The Annual Conference on Neural Information Processing Systems 16. [PDF]
- Ji Zhu and Trevor Hastie (2001) Kernel logistic regression and the import vector machine. The Annual Conference on Neural Information Processing Systems 14. [PDF][CODE]
Comments
- William Li and Ji Zhu (2014) Comment on "Screening strategies in the presence of interactions" by D. Draguljic, D. Woods, A. Dean, S. Lewis and A. Vine. Technometrics 56(1):21-22. [PDF]
- Peter Bickel, Elizaveta Levina, Adam Rothman, and Ji Zhu (2012) Comment on "Minimax estimation of large covariance matrices under L1-norm" by T. Cai and H. Zhou. Statistica Sinica 22(4):1367-1370. [PDF]
- Adam Rothman, Elizaveta Levina, and Ji Zhu (2010) Discussion of "Stability selection" by N. Meinshausen and P. Buhlmann. Journal of the Royal Statistical Society, Series B 72:465-467. [PDF]
- Elizaveta Levina and Ji Zhu (2008) Discussion of "Sure independence screening for ultra-high dimensional feature space" by J. Fan and J. Lv. Journal of the Royal Statistical Society, Series B 70:897-898. [PDF]
- Trevor Hastie and Ji Zhu (2007) Discussion of "Support vector machines with applications" by J. Moguerza and A. Munoz. Statistical Science 21:352-357. [PDF]
- Saharon Rosset and Ji Zhu (2004) Discussion of "Least angle regression" by B. Efron, T. Hastie, I. Johnstone and R. Tibshirani. Annals of Statistics 32:469-475. [PDF]
- Jerry Friedman, Trevor Hastie, Saharon Rosset, Rob Tibshirani, and Ji Zhu (2004) Discussion of three boosting papers on "Consistency in boosting". The three papers are by (1) W. Jiang (2) G. Lugosi, N. Vayatis and (3) T. Zhang. Annals of Statistics 32:102-107. [PDF]
Last modified: Wed Apr 15 2020