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ORCIDHeng Lian 0002
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Other persons with the same name
- Heng Lian — disambiguation page
- Heng Lian 0001
![[0000-0003-3240-4221]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-3240-4221")
— College of William and Mary, School of Computing, Department of Data Science, Williamsburg, VA, USA (and 2 more) - Heng Lian 0003
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2020 – today
- 2026
[j76]Wenqi Lu, Hongmei Lin, Heng Lian:
Randomized tensor decomposition and optimization in the tucker and tensor train formats. J. Comput. Appl. Math. 482: 117344 (2026)- [j75]Xiaoqi Jiao, Heng Lian, Jiamin Liu, Yingying Zhang:
Linear convergence of proximal gradient method for linear sparse SVM. Neural Networks 194: 108162 (2026) - 2025
- [j74]Changxin Yang, Zhongyi Zhu, Hongmei Lin, Zengyan Fan, Heng Lian:
Distributed iterative hard thresholding for variable selection in Tobit models. Comput. Stat. Data Anal. 211: 108227 (2025) - [j73]Yujing Shao, Zhaohan Hou, Lei Wang, Heng Lian:
Optimal decorrelated score subsampling for Cox regression with massive survival data. Neurocomputing 638: 130090 (2025) - [j72]Yujing Shao, Lei Wang, Heng Lian:
Optimal Decorrelated Score Subsampling for High-Dimensional Generalized Linear Models Under Measurement Constraints. J. Comput. Graph. Stat. 34(2): 530-539 (2025) - [j71]Jianwei Shi, Yue Wang, Zhongyi Zhu, Heng Lian:
Decentralized Learning of Quantile Regression: A Smoothing Approach. J. Comput. Graph. Stat. 34(3): 1091-1101 (2025) - [j70]Yujing Shao, Lei Wang, Heng Lian, Haiying Wang:
Optimal subsampling for high-dimensional partially linear models via machine learning methods. J. Mach. Learn. Res. 26: 198:1-198:70 (2025) - [j69]Xing Li, Lei Wang, Heng Lian:
Optimal distributed subsampling for expected shortfall regression via Neyman-orthogonal score. Knowl. Based Syst. 318: 113529 (2025) - [j68]Jiamin Liu, Lei Wang, Heng Lian:
Improved analysis of supervised learning in the RKHS with random features: Beyond least squares. Neural Networks 184: 107091 (2025) - [j67]Yujing Shao, Lei Wang, Heng Lian:
Optimal distributed subsampling under heterogeneity. Stat. Comput. 35(2): 26 (2025) - [j66]Jin Liu, Wei Ma, Lei Wang, Heng Lian:
Sparse and debiased Lasso estimation and statistical inference for long time series via divide-and-conquer. Stat. Comput. 35(3): 72 (2025) - [j65]Jichen Yang, Lei Wang, Heng Lian:
Debiased transfer learning estimation and inference for multinomial regression. Stat. Comput. 35(3): 73 (2025) - [j64]Zihao Song, Jicai Liu, Lei Wang, Heng Lian, Weihua Zhao:
Sparse estimation and inference for prediction-powered semi-supervised linear regression. Stat. Comput. 35(5): 123 (2025) - [j63]Jichen Yang, Lei Wang, Heng Lian:
Communication-efficient transfer learning for adaptive Huber regression. Stat. Comput. 35(5): 147 (2025) - [j62]Yining Wu, Lei Wang, Heng Lian:
Heterogeneity-aware debiased machine learning for high-dimensional partially linear models. Stat. Comput. 35(6): 202 (2025) - [j61]Junzhuo Gao, Heng Lian:
Decentralized Nonconvex Low-rank Matrix Recovery. IEEE Trans. Image Process. 34: 4806-4813 (2025) - [j60]Ziyuan Wang, Jin Liu, Jun Shao, Heng Lian, Lei Wang:
Distributed Semi-Supervised Inference for Generalized Linear Models With Block-Wise Missing Covariates. IEEE Trans. Inf. Theory 71(10): 7815-7841 (2025) - [j59]Jiamin Liu, Heng Lian:
Kernel-Based Decentralized Policy Evaluation for Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 36(6): 10371-10380 (2025) - 2024
- [j58]Chunyang Zhu, Lei Wang, Weihua Zhao, Heng Lian:
Image classification based on tensor network DenseNet model. Appl. Intell. 54(8): 6624-6636 (2024) - [j57]Wenqi Lu, Zhongyi Zhu, Rui Li, Heng Lian:
Statistical performance of quantile tensor regression with convex regularization. J. Multivar. Anal. 200: 105249 (2024) - [j56]Yue Wang, Wenqi Lu, Heng Lian:
Distributed statistical estimation in quantile regression over a network. Signal Process. 222: 109512 (2024) - [j55]Wangli Xu, Jiamin Liu, Heng Lian:
Distributed Estimation of Support Vector Machines for Matrix Data. IEEE Trans. Neural Networks Learn. Syst. 35(5): 6643-6653 (2024) - 2023
- [j54]Yaohong Yang, Lei Wang, Jiamin Liu, Rui Li, Heng Lian:
Communication-efficient estimation of quantile matrix regression for massive datasets. Comput. Stat. Data Anal. 187: 107812 (2023) - [j53]Jiamin Liu, Wangli Xu, Yue Wang, Heng Lian:
Value iteration for streaming data on a continuous space with gradient method in an RKHS. Neural Networks 166: 437-445 (2023) - [j52]Jiamin Liu, Wangli Xu, Fode Zhang, Heng Lian:
Properties of Standard and Sketched Kernel Fisher Discriminant. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 10596-10602 (2023) - [j51]Jiamin Liu, Heng Lian:
On Optimal Learning With Random Features. IEEE Trans. Neural Networks Learn. Syst. 34(11): 9536-9541 (2023) - [j50]Yue Wang, Heng Lian:
On Linear Convergence of ADMM for Decentralized Quantile Regression. IEEE Trans. Signal Process. 71: 3945-3955 (2023) - 2022
- [j49]Yue Wang, Yan Zhou, Rui Li, Heng Lian:
Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space. Comput. Stat. Data Anal. 168: 107388 (2022) - 2021
- [j48]Fode Zhang, Rui Li, Heng Lian:
Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection. Inf. Sci. 547: 244-254 (2021) - [j47]Heng Lian, Jiamin Liu, Zengyan Fan:
Distributed learning for sketched kernel regression. Neural Networks 143: 368-376 (2021) - [j46]Heng Lian:
Learning Rate for Convex Support Tensor Machines. IEEE Trans. Neural Networks Learn. Syst. 32(8): 3755-3760 (2021) - [j45]Yue Wang, Weiping Zhang, Heng Lian:
Distributed Partially Linear Additive Models With a High Dimensional Linear Part. IEEE Trans. Signal Inf. Process. over Networks 7: 611-625 (2021) - 2020
- [j44]Xia Cui, Hongmei Lin, Heng Lian:
Partially functional linear regression in reproducing kernel Hilbert spaces. Comput. Stat. Data Anal. 150: 106978 (2020) - [j43]Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu:
A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. Comput. Stat. Data Anal. 152: 107039 (2020) - [j42]Fode Zhang, Xuejun Wang, Rui Li, Heng Lian:
Randomized sketches for sparse additive models. Neurocomputing 385: 80-87 (2020) - [j41]Hanbing Zhu, Rui Li, Riquan Zhang, Heng Lian:
Nonlinear functional canonical correlation analysis via distance covariance. J. Multivar. Anal. 180: 104662 (2020) - [j40]Heng Lian, Fode Zhang, Wenqi Lu:
Randomized sketches for kernel CCA. Neural Networks 127: 29-37 (2020) - [j39]Weihua Zhao, Fode Zhang, Heng Lian:
Debiasing and Distributed Estimation for High-Dimensional Quantile Regression. IEEE Trans. Neural Networks Learn. Syst. 31(7): 2569-2577 (2020)
2010 – 2019
- 2019
- [j38]Lili Yue, Gaorong Li, Heng Lian, Xiang Wan:
Regression adjustment for treatment effect with multicollinearity in high dimensions. Comput. Stat. Data Anal. 134: 17-35 (2019) - 2017
- [j37]Yujie Li, Gaorong Li, Heng Lian, Tiejun Tong:
Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models. J. Multivar. Anal. 155: 133-150 (2017) - 2016
- [j36]Kaifeng Zhao, Heng Lian:
The Expectation-Maximization approach for Bayesian quantile regression. Comput. Stat. Data Anal. 96: 1-11 (2016) - [j35]Weihua Zhao, Heng Lian, Riquan Zhang, Peng Lai:
Estimation and variable selection for proportional response data with partially linear single-index models. Comput. Stat. Data Anal. 96: 40-56 (2016) - [j34]Heng Lian, Hua Liang:
Separation of linear and index covariates in partially linear single-index models. J. Multivar. Anal. 143: 56-70 (2016) - [j33]Heng Lian, Yongdai Kim:
Nonconvex penalized reduced rank regression and its oracle properties in high dimensions. J. Multivar. Anal. 143: 383-393 (2016) - [j32]Xingyu Tang, Heng Lian:
Mean and quantile boosting for partially linear additive models. Stat. Comput. 26(5): 997-1008 (2016) - 2015
- [j31]Ye Tian, Heng Lian:
A Note on Application of Nesterov's Method in Solving Lasso-Type Problems. Commun. Stat. Simul. Comput. 44(7): 1673-1682 (2015) - [j30]Heng Lian, Sanying Feng, Kaifeng Zhao:
Parametric and semiparametric reduced-rank regression with flexible sparsity. J. Multivar. Anal. 136: 163-174 (2015) - [j29]Heng Lian:
Minimax prediction for functional linear regression with functional responses in reproducing kernel Hilbert spaces. J. Multivar. Anal. 140: 395-402 (2015) - [j28]Heng Lian, Jie Meng, Zengyan Fan:
Simultaneous estimation of linear conditional quantiles with penalized splines. J. Multivar. Anal. 141: 1-21 (2015) - [j27]Heng Lian:
Quantile regression for dynamic partially linear varying coefficient time series models. J. Multivar. Anal. 141: 49-66 (2015) - [j26]Heng Lian, Jie Meng, Kaifeng Zhao:
Spline estimator for simultaneous variable selection and constant coefficient identification in high-dimensional generalized varying-coefficient models. J. Multivar. Anal. 141: 81-103 (2015) - [j25]Gaorong Li, Peng Lai, Heng Lian:
Variable selection and estimation for partially linear single-index models with longitudinal data. Stat. Comput. 25(3): 579-593 (2015) - [j24]Yuao Hu, Kaifeng Zhao, Heng Lian:
Bayesian quantile regression for partially linear additive models. Stat. Comput. 25(3): 651-668 (2015) - 2014
- [j23]Heng Lian, Pang Du, Yuanzhang Li, Hua Liang:
Partially linear structure identification in generalized additive models with NP-dimensionality. Comput. Stat. Data Anal. 80: 197-208 (2014) - [j22]Kaifeng Zhao, Heng Lian:
Variational inferences for partially linear additive models with variable selection. Comput. Stat. Data Anal. 80: 223-239 (2014) - [j21]Heng Lian:
Semiparametric Bayesian information criterion for model selection in ultra-high dimensional additive models. J. Multivar. Anal. 123: 304-310 (2014) - [j20]Heng Lian, Gaorong Li:
Series expansion for functional sufficient dimension reduction. J. Multivar. Anal. 124: 150-165 (2014) - [j19]Heng Lian, Jianbo Li, Xingyu Tang:
SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part. J. Multivar. Anal. 125: 50-64 (2014) - [j18]Jianbo Li, Zhensheng Huang, Heng Lian:
Empirical likelihood inference for general transformation models with right censored data. Stat. Comput. 24(6): 985-995 (2014) - 2013
- [j17]Gaorong Li, Heng Lian, Sanying Feng, Lixing Zhu:
Automatic variable selection for longitudinal generalized linear models. Comput. Stat. Data Anal. 61: 174-186 (2013) - [j16]Heng Lian, Jianbo Li, Yuao Hu:
Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality. Comput. Stat. Data Anal. 63: 99-112 (2013) - [j15]Lichun Wang, Yuan You, Heng Lian:
A simple and efficient algorithm for fused lasso signal approximator with convex loss function. Comput. Stat. 28(4): 1699-1714 (2013) - [j14]Zhaoping Hong, Heng Lian:
Sparse-smooth regularized singular value decomposition. J. Multivar. Anal. 117: 163-174 (2013) - [j13]Peng Lai, Gaorong Li, Heng Lian:
Quadratic inference functions for partially linear single-index models with longitudinal data. J. Multivar. Anal. 118: 115-127 (2013) - [j12]Xingyu Tang, Jianbo Li, Heng Lian:
Empirical likelihood for partially linear proportional hazards models with growing dimensions. J. Multivar. Anal. 121: 22-32 (2013) - [j11]Yuao Hu, Robert B. Gramacy, Heng Lian:
Bayesian quantile regression for single-index models. Stat. Comput. 23(4): 437-454 (2013) - 2012
- [j10]Zhaoping Hong, Heng Lian:
BOPA: A Bayesian hierarchical model for outlier expression detection. Comput. Stat. Data Anal. 56(12): 4146-4156 (2012) - [j9]Zhaoping Hong, Heng Lian:
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates. J. Multivar. Anal. 103(1): 58-67 (2012) - [j8]Peng Lai, Qihua Wang, Heng Lian:
Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data. J. Multivar. Anal. 105(1): 422-432 (2012) - [j7]Heng Lian:
On feature selection with principal component analysis for one-class SVM. Pattern Recognit. Lett. 33(9): 1027-1031 (2012) - [j6]Robert B. Gramacy, Heng Lian:
Gaussian Process Single-Index Models as Emulators for Computer Experiments. Technometrics 54(1): 30-41 (2012) - 2011
- [j5]Gaorong Li, Liugen Xue, Heng Lian:
Semi-varying coefficient models with a diverging number of components. J. Multivar. Anal. 102(7): 1166-1174 (2011) - 2010
- [j4]Heng Lian:
Sparse Bayesian hierarchical modeling of high-dimensional clustering problems. J. Multivar. Anal. 101(7): 1728-1737 (2010) - [j3]Aditya Chopra, Heng Lian:
Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images. Pattern Recognit. 43(8): 2609-2619 (2010)
2000 – 2009
- 2009
- [j2]Heng Lian:
Bayesian Nonlinear Principal Component Analysis Using Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 31(4): 749-754 (2009) - 2007
- [j1]Heng Lian:
On the Consistency of Bayesian Function Approximation Using Step Functions. Neural Comput. 19(11): 2871-2880 (2007)
Coauthor Index
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