Selected Journal Papers (Statistical Theory and Methodology)
- Sheng Zhang, Rui Song, Wenbin Lv, and Ji Zhu (2024+) Distributed community detection in large networks. Journal of Machine Learning Research. Accepted.
- Xianshi Yu and Ji Zhu (2024+) Network community detection using higher-order structures. Biometrika. Accepted.
- Jiangzhou Wang, Jingfei Zhang, Binghui Liu, Ji Zhu, and Jianhua Guo (2023) Fast network community detection with profile-pseudo likelihood methods. Journal of the American Statistical Association 118(542):1359-1372. [PDF][CODE]
- Weijing Tang, Kevin He, Gongjun Xu, and Ji Zhu (2023) Survival analysis via ordinary differential equations. Journal of the American Statistical Association 118(544):2406-2421. [PDF][CODE] (One of three winning papers in the 2020 ASA Student Paper Competition sponsored by the Nonparametric Statistics Section)
- Tianxi Li, Yun-Jhong Wu, Elizaveta Levina, and Ji Zhu (2023) Link prediction for egocentrically sampled networks. Journal of Computational and Graphical Statistics 32(4):1296-1319. [PDF][CODE]
- Tianxi Li, Elizaveta Levina, and Ji Zhu (2023) Community models for networks observed through edge nominations. Journal of Machine Learning Research 24:1-36. [PDF][CODE]
- Xuefei Zhang, Gongjun Xu, and Ji Zhu (2022) Joint latent space models for network data with high-dimensional node variables. Biometrika 109(3):707-720. [PDF][CODE]
- Weijing Tang, Jiaqi Ma, Qiaozhu Mei, and Ji Zhu (2022) A scalable continuous-time survival model through ordinary differential equation networks. Journal of Machine Learning Research 23:1-29. [PDF][CODE]
- Peter MacDonald, Elizaveta Levina, and Ji Zhu (2022) Latent space models for multiplex networks with shared structure. Biometrika 109(3):683-706. [PDF][CODE] (One of four winning papers in the 2021 ASA Student Paper Competition sponsored by the Statistical Learning and Data Science Section)
- Tianwen Ma, Yang Li, Jane Huggins, Ji Zhu, and Jian Kang (2022) Bayesian inferences on neural activity in EEG-based brain-computer interface. Journal of the American Statistical Association 117(539):1122-1133. [PDF][CODE]
- Kevin He, Ji Zhu, Jian Kang, and Yi Li (2022) Stratified Cox models with time-varying effects for national kidney transplant patients: a new block-wise steepest ascent method. Biometrics 78(3):1221-1232. [PDF][CODE]
- Yuehan Yang, Ji Zhu, and Edward George (2021) MuSP: a multi-step screening procedure for sparse recovery. Stat 10:e352. [PDF][CODE]
- Yanming Li, Bin Nan, and Ji Zhu (2021) A structured brain-wide and genome-wide association study using ADNI PET images. Canadian Journal of Statistics 49(1):182-202. [PDF][CODE]
- Jianwei Hu, Jingfei Zhang, Hong Qin, Ting Yan, and Ji Zhu (2021) Using maximum entry-wise deviation to test the goodness-of-fit for stochastic block models. Journal of the American Statistical Association 116(535):1373-1382. [PDF][CODE]
- Yuan Zhang, Elizaveta Levina, and Ji Zhu (2020) Detecting overlapping communities in networks using spectral methods. SIAM Journal on Mathematics of Data Science 2(2):265-283. [PDF][CODE]
- Yuehan Yang and Ji Zhu (2020) A two-step method for estimating high-dimensional Gaussian graphical models. Science China Mathematics 63(6):1203-1218. [PDF][CODE]
- Weijing Tang, Jiaqi Ma, Akbar Waljee, and Ji Zhu (2020) Semi-supervised joint learning for longitudinal clinical events classification using neural network models. Stat 9:e305. (Special issue on deep learning) [PDF][CODE]
- Tianxi Li, Cheng Qian, Elizaveta Levina, and Ji Zhu (2020) High-dimensional Gaussian graphical models on network-linked data. Journal of Machine Learning Research 21(74):1-45. [PDF][CODE]
- Tianxi Li, Elizaveta Levina, and Ji Zhu (2020) Network cross-validation by edge sampling (with discussion). Biometrika 107(2):257-276. [PDF][CODE] (One of three winning papers in the 2017 ASA Student Paper Competition sponsored by the Nonparametric Statistics Section)
- Tianxi Li, Elizaveta Levina, and Ji Zhu (2019) Prediction models for network-linked data. Annals of Applied Statistics 13(1):132-164. [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 75(2):551-561. [PDF][CODE]
- Yan Zhou, Pei Wang, Xianlong Wang, Ji Zhu, and Peter Song (2017) Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis. Genetic Epidemiology 41(1):70-80. [PDF]
- 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)
- Xuejing Wang, Bin Nan, Ji Zhu, Robert Koeppe, and Kirk Frey (2017) Classification of ADNI PET images via regularized 3D functional data analysis. Biostatistics & Epidemiology 1(1):3-19. [PDF]
- Wenwu He, James Kwok, Ji Zhu, and Yang Liu (2017) A note on the unification of adaptive online learning. IEEE Transactions on Neural Networks and Learning Systems 28(5):1178-1191. [PDF]
- 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]
- Jianhua Zhao, Lei Shi, and Ji Zhu (2015) Two-stage regularized linear discriminant analysis for 2-D data. IEEE Transactions on Neural Networks and Learning Systems 26(8):1669-1681. [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]
- Ashin Mukherjee and Ji Zhu (2011) Reduced rank ridge regression and its kernel extension. Statistical Analysis and Data Mining 4(6):612-622. [PDF][CODE] (One of five winning papers in the 2011 ASA Student Paper Competition sponsored by the Statistical Learning and Data Mining Section)
- 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)
- Li Wang and Ji Zhu (2010) Image denoising via solution paths. Annals of Operations Research 174(1):3-17. (Special issue on data mining) [PDF]
- 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)
- Ji Zhu, Hui Zou, Saharon Rosset, and Trevor Hastie (2009) Multi-class adaboost. Statistics and Its Interface 2(3):349-360. (Special issue on data mining and machine learning) [PDF][CODE]
- 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]
- Adam Rothman, Peter Bickel, Elizaveta Levina, and Ji Zhu (2008) Sparse permutation invariant covariance estimation. Electronic Journal of Statistics 2:494-515. [PDF] (One of four winning papers in the 2008 ASA Student Paper Competition sponsored by the Statistical Computing Section)
- 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 Refereed Conference Papers
- Xuefei Zhang, Songkai Xue, and Ji Zhu (2020) A flexible latent space model for multilayer networks. International Conference on Machine Learning 37. (ICML'20) [PDF]
- Jiaqi Ma, Weijing Tang, Ji Zhu, and Qiaozhu Mei (2019) A flexible generative framework for graph-based semi-supervised learning. Neural Information Processing Systems 32. (NeurIPS'19) [PDF]
- Saharon Rosset, Nathan Srebro, Grzegorz Swirszcz, and Ji Zhu (2007) L1 regularization in infinite dimensional feature spaces. Conference on Learning Theory 20. (COLT'07) [PDF]
- Lacey Gunter and Ji Zhu (2005) Computing the solution path for the regularized support vector regression. Neural Information Processing Systems 18. (NeurIPS'05) [PDF][CODE]
- Saharon Rosset, Ji Zhu, Hui Zou, and Trevor Hastie (2004) A method for inferring label sampling mechanisms in semi-supervised learning. Neural Information Processing Systems 17. (NeurIPS'04) [PDF]
- Trevor Hastie, Saharon Rosset, Rob Tibshirani, and Ji Zhu (2004) The entire regularization path for the support vector machine. Neural Information Processing Systems 17. (NeurIPS'04) [PDF][CODE] (One of the oral presentation papers at NeurIPS 2004)
- Ji Zhu, Saharon Rosset, Rob Tibshirani, and Trevor Hastie (2003) 1-norm support vector machines. Neural Information Processing Systems 16. (NeurIPS'03) [PDF][CODE] (One of the spotlight papers at NeurIPS 2003)
- Saharon Rosset, Ji Zhu, and Trevor Hastie (2003) Margin maximizing loss functions. Neural Information Processing Systems 16. (NeurIPS'03) [PDF]
- Ji Zhu and Trevor Hastie (2001) Kernel logistic regression and the import vector machine. Neural Information Processing Systems 14. (NeurIPS'01) [PDF][CODE]
Comments
- Nam-Hee Choi, Kerby Shedden, Gongjun Xu, Xuefei Zhang, and Ji Zhu (2020) "Comment: ridge regression, ranking variables and improved principal component regression" Technometrics 62(4):451-455. [PDF]
- 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: Tue Mar 26 2024