Variational Local Structure Estimation for Image Super-Resolution. - Heng Lian (ICIP, 2006)

On the Consistency of Bayesian Function Approximation Using Step Functions. - Heng Lian (Neural Comput., 2007)

Automated mapping of large-scale chromatin structure in ENCODE. - Heng Lian; William A. Thompson; Robert E. Thurman; John A. Stamatoyannopoulos; William Stafford Noble; Charles E. Lawrence (Bioinform., 2008)

Bayesian Nonlinear Principal Component Analysis Using Random Fields. - Heng Lian (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Sparse Bayesian hierarchical modeling of high-dimensional clustering problems. - Heng Lian (J. Multivar. Anal., 2010)

Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images. - Aditya Chopra; Heng Lian (Pattern Recognit., 2010)

Semi-varying coefficient models with a diverging number of components. - Gaorong Li; Liugen Xue; Heng Lian (J. Multivar. Anal., 2011)

BOPA: A Bayesian hierarchical model for outlier expression detection. - Zhaoping Hong; Heng Lian (Comput. Stat. Data Anal., 2012)

Time-varying coefficient estimation in differential equation models with noisy time-varying covariates. - Zhaoping Hong; Heng Lian (J. Multivar. Anal., 2012)

Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data. - Peng Lai; Qihua Wang; Heng Lian (J. Multivar. Anal., 2012)

On feature selection with principal component analysis for one-class SVM. - Heng Lian (Pattern Recognit. Lett., 2012)

Gaussian Process Single-Index Models as Emulators for Computer Experiments. - Robert B. Gramacy; Heng Lian (Technometrics, 2012)

Automatic variable selection for longitudinal generalized linear models. - Gaorong Li; Heng Lian; Sanying Feng; Lixing Zhu (Comput. Stat. Data Anal., 2013)

Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality. - Heng Lian; Jianbo Li; Yuao Hu (Comput. Stat. Data Anal., 2013)

A simple and efficient algorithm for fused lasso signal approximator with convex loss function. - Lichun Wang 003; Yuan You; Heng Lian (Comput. Stat., 2013)

Sparse-smooth regularized singular value decomposition. - Zhaoping Hong; Heng Lian (J. Multivar. Anal., 2013)

Quadratic inference functions for partially linear single-index models with longitudinal data. - Peng Lai; Gaorong Li; Heng Lian (J. Multivar. Anal., 2013)

Empirical likelihood for partially linear proportional hazards models with growing dimensions. - Xingyu Tang; Jianbo Li; Heng Lian (J. Multivar. Anal., 2013)

Bayesian quantile regression for single-index models. - Yuao Hu; Robert B. Gramacy; Heng Lian (Stat. Comput., 2013)

Partially linear structure identification in generalized additive models with NP-dimensionality. - Heng Lian; Pang Du; Yuanzhang Li; Hua Liang (Comput. Stat. Data Anal., 2014)

Variational inferences for partially linear additive models with variable selection. - Kaifeng Zhao 001; Heng Lian (Comput. Stat. Data Anal., 2014)

Semiparametric Bayesian information criterion for model selection in ultra-high dimensional additive models. - Heng Lian (J. Multivar. Anal., 2014)

Series expansion for functional sufficient dimension reduction. - Heng Lian; Gaorong Li (J. Multivar. Anal., 2014)

SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part. - Heng Lian; Jianbo Li; Xingyu Tang (J. Multivar. Anal., 2014)

Empirical likelihood inference for general transformation models with right censored data. - Jianbo Li; Zhensheng Huang; Heng Lian (Stat. Comput., 2014)

A Note on Application of Nesterov's Method in Solving Lasso-Type Problems. - Ye Tian; Heng Lian (Commun. Stat. Simul. Comput., 2015)

Minimax prediction for functional linear regression with functional responses in reproducing kernel Hilbert spaces. - Heng Lian (J. Multivar. Anal., 2015)

Quantile regression for dynamic partially linear varying coefficient time series models. - Heng Lian (J. Multivar. Anal., 2015)

Parametric and semiparametric reduced-rank regression with flexible sparsity. - Heng Lian; Sanying Feng; Kaifeng Zhao 001 (J. Multivar. Anal., 2015)

Simultaneous estimation of linear conditional quantiles with penalized splines. - Heng Lian; Jie Meng; Zengyan Fan (J. Multivar. Anal., 2015)

Spline estimator for simultaneous variable selection and constant coefficient identification in high-dimensional generalized varying-coefficient models. - Heng Lian; Jie Meng; Kaifeng Zhao 001 (J. Multivar. Anal., 2015)

Bayesian quantile regression for partially linear additive models. - Yuao Hu; Kaifeng Zhao 001; Heng Lian (Stat. Comput., 2015)

Variable selection and estimation for partially linear single-index models with longitudinal data. - Gaorong Li; Peng Lai; Heng Lian (Stat. Comput., 2015)

A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data. - Sanying Feng; Heng Lian; Liugen Xue (Comput. Stat. Data Anal., 2016)

Reduced rank regression with possibly non-smooth criterion functions: An empirical likelihood approach. - Sanying Feng; Heng Lian; Fukang Zhu (Comput. Stat. Data Anal., 2016)

Robust closed-form estimators for the integer-valued GARCH (1, 1) model. - Qi Li; Heng Lian; Fukang Zhu (Comput. Stat. Data Anal., 2016)

The Expectation-Maximization approach for Bayesian quantile regression. - Kaifeng Zhao 001; Heng Lian (Comput. Stat. Data Anal., 2016)

Estimation and variable selection for proportional response data with partially linear single-index models. - Weihua Zhao; Heng Lian; Riquan Zhang; Peng Lai (Comput. Stat. Data Anal., 2016)

Minimax convergence rates for kernel CCA. - Zengyan Fan; Heng Lian (J. Multivar. Anal., 2016)

Posterior convergence for Bayesian functional linear regression. - Heng Lian; Taeryon Choi; Jie Meng; Seongil Jo (J. Multivar. Anal., 2016)

Nonconvex penalized reduced rank regression and its oracle properties in high dimensions. - Heng Lian; Yongdai Kim (J. Multivar. Anal., 2016)

Separation of linear and index covariates in partially linear single-index models. - Heng Lian; Hua Liang (J. Multivar. Anal., 2016)

Mean and quantile boosting for partially linear additive models. - Xingyu Tang; Heng Lian (Stat. Comput., 2016)

Composite quantile regression for correlated data. - Weihua Zhao; Heng Lian; Xinyuan Song (Comput. Stat. Data Anal., 2017)

Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions. - Heng Lian; Zengyan Fan (J. Mach. Learn. Res., 2017)

Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models. - Yujie Li; Gaorong Li; Heng Lian; Tiejun Tong (J. Multivar. Anal., 2017)

Estimation and model identification of longitudinal data time-varying nonparametric models. - Shu Liu; Jinhong You; Heng Lian (J. Multivar. Anal., 2017)

Estimation and variable selection for quantile partially linear single-index models. - Yuankun Zhang; Heng Lian; Yan Yu (J. Multivar. Anal., 2017)

Quantile index coefficient model with variable selection. - Weihua Zhao; Heng Lian (J. Multivar. Anal., 2017)

Estimation and testing for time-varying quantile single-index models with longitudinal data. - Jianbo Li; Heng Lian; Xuejun Jiang; Xinyuan Song (Comput. Stat. Data Anal., 2018)

A principal varying-coefficient model for quantile regression: Joint variable selection and dimension reduction. - Weihua Zhao; Xuejun Jiang; Heng Lian (Comput. Stat. Data Anal., 2018)

Time-varying quantile single-index model for multivariate responses. - Weihua Zhao; Yan Zhou; Heng Lian (Comput. Stat. Data Anal., 2018)

Quantile regression for additive coefficient models in high dimensions. - Zengyan Fan; Heng Lian (J. Multivar. Anal., 2018)

On the sign consistency of the Lasso for the high-dimensional Cox model. - Shaogao Lv; Mengying You; Huazhen Lin; Heng Lian; Jian Huang 003 (J. Multivar. Anal., 2018)

Pursuit of dynamic structure in quantile additive models with longitudinal data. - Xia Cui; Weihua Zhao; Heng Lian; Hua Liang (Comput. Stat. Data Anal., 2019)

Estimation for single-index models via martingale difference divergence. - Jicai Liu; Peirong Xu; Heng Lian (Comput. Stat. Data Anal., 2019)

Regression adjustment for treatment effect with multicollinearity in high dimensions. - Lili Yue; Gaorong Li; Heng Lian; Xiang Wan (Comput. Stat. Data Anal., 2019)

Reduced rank modeling for functional regression with functional responses. - Hongmei Lin; Xuejun Jiang; Heng Lian; Weiping Zhang (J. Multivar. Anal., 2019)

Rank reduction for high-dimensional generalized additive models. - Hongmei Lin; Heng Lian; Hua Liang (J. Multivar. Anal., 2019)

Estimation and testing for partially functional linear errors-in-variables models. - Hanbing Zhu; Riquan Zhang; Zhou Yu; Heng Lian; Yanghui Liu (J. Multivar. Anal., 2019)

Partially functional linear regression in reproducing kernel Hilbert spaces. - Xia Cui; Hongmei Lin; Heng Lian (Comput. Stat. Data Anal., 2020)

A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. - Shaogao Lv; Zengyan Fan; Heng Lian; Taiji Suzuki; Kenji Fukumizu (Comput. Stat. Data Anal., 2020)

Randomized sketches for sparse additive models. - Fode Zhang; Xuejun Wang; Rui Li 078; Heng Lian (Neurocomputing, 2020)

Nonlinear functional canonical correlation analysis via distance covariance. - Hanbing Zhu; Rui Li 078; Riquan Zhang; Heng Lian (J. Multivar. Anal., 2020)

Randomized sketches for kernel CCA. - Heng Lian; Fode Zhang; Wenqi Lu (Neural Networks, 2020)

Debiasing and Distributed Estimation for High-Dimensional Quantile Regression. - Weihua Zhao; Fode Zhang; Heng Lian (IEEE Trans. Neural Networks Learn. Syst., 2020)

Sketched quantile additive functional regression. - Yingying Zhang; Heng Lian (Neurocomputing, 2021)

Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection. - Fode Zhang; Rui Li 078; Heng Lian (Inf. Sci., 2021)

Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces. - Guangren Yang; Xiaohui Liu; Heng Lian (J. Complex., 2021)

Distributed learning for sketched kernel regression. - Heng Lian; Jiamin Liu; Zengyan Fan (Neural Networks, 2021)

Learning Rate for Convex Support Tensor Machines. - Heng Lian (IEEE Trans. Neural Networks Learn. Syst., 2021)

Distributed Partially Linear Additive Models With a High Dimensional Linear Part. - Yue Wang 147; Weiping Zhang; Heng Lian (IEEE Trans. Signal Inf. Process. over Networks, 2021)

Online Deep Learning from Doubly-Streaming Data. - Heng Lian; John Scovil Atwood; Bojian Hou; Jian Wu 006; Yi He 007 (ACM Multimedia, 2022)

Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space. - Heng Lian (NeurIPS, 2022)

Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space. - Yue Wang 147; Yan Zhou; Rui Li; Heng Lian (Comput. Stat. Data Anal., 2022)

Life History Recorded in the Vagino-cervical Microbiome Along with Multi-omes. - Zhuye Jie; Chen Chen; Lilan Hao; Fei Li; Liju Song; Xiaowei Zhang; Jie Zhu; Liu Tian; Xin Tong; Kaiye Cai; Zhe Zhang; Yanmei Ju; et al. (Genom. Proteom. Bioinform., 2022)

Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions. - Shaogao Lv; Heng Lian (J. Mach. Learn. Res., 2022)

Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification. - Yingying Zhang; Yan-Yong Zhao; Heng Lian (J. Mach. Learn. Res., 2022)

Semiparametric penalized quadratic inference functions for longitudinal data in ultra-high dimensions. - Brittany Green; Heng Lian; Yan Yu; Tianhai Zu (J. Multivar. Anal., 2023)

Functional additive expectile regression in the reproducing kernel Hilbert space. - Yuzi Liu; Ling Peng; Qing Liu; Heng Lian; Xiaohui Liu (J. Multivar. Anal., 2023)

On Linear Convergence of ADMM for Decentralized Quantile Regression. - Yue Wang 142; Heng Lian (IEEE Trans. Signal Process., 2023)

Best subset selection for high-dimensional non-smooth models using iterative hard thresholding. - Yue Wang; Wenqi Lu; Heng Lian (Inf. Sci., 2023)

Image recognition and classification with HOG based on nonlinear support tensor machine. - Chunyang Zhu; Weihua Zhao; Heng Lian (Multim. Tools Appl., 2023)

Semiparametric function-on-function quantile regression model with dynamic single-index interactions. - Hanbing Zhu; Yuanyuan Zhang; Yehua Li; Heng Lian (Comput. Stat. Data Anal., 2023)

Properties of Standard and Sketched Kernel Fisher Discriminant. - Jiamin Liu; Wangli Xu; Fode Zhang; Heng Lian (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Value iteration for streaming data on a continuous space with gradient method in an RKHS. - Jiamin Liu; Wangli Xu; Yue Wang; Heng Lian (Neural Networks, 2023)

Communication-efficient estimation of quantile matrix regression for massive datasets. - Yaohong Yang; Lei Wang; Jiamin Liu; Rui Li; Heng Lian (Comput. Stat. Data Anal., 2023)

On Optimal Learning With Random Features. - Jiamin Liu; Heng Lian (IEEE Trans. Neural Networks Learn. Syst., 2023)

Urban Functional Zone Delineation Aided by Vision Foundation Models. - Heng Lian; Yi He 007 (ICDM (Workshops), 2024)

Utilitarian Online Learning from Open-World Soft Sensing. - Heng Lian; Yu Huang 017; Xingquan Zhu 001; Yi He 007 (ICDM, 2024)

HGDL: Heterogeneous Graph Label Distribution Learning. - Yufei Jin; Heng Lian; Yi He 007; Xingquan Zhu 001 (NeurIPS, 2024)

Linear convergence of decentralized estimation for statistical estimation using gradient method. - Wangli Xu; Kehan Wang; Heng Lian (Neurocomputing, 2024)

Adaptive Huber trace regression with low-rank matrix parameter via nonconvex regularization. - Xiangyong Tan; Ling Peng; Heng Lian; Xiaohui Liu (J. Complex., 2024)

More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization. - Xu Liu 024; Heng Lian; Jian Huang 003 (J. Mach. Learn. Res., 2024)

Robust low tubal rank tensor recovery via L2E criterion. - Zihao Song; Xiangjian Xu; Heng Lian; Weihua Zhao (Pattern Recognit., 2024)

Distributed statistical estimation in quantile regression over a network. - Yue Wang 142; Wenqi Lu; Heng Lian (Signal Process., 2024)

Online Learning From Evolving Feature Spaces With Deep Variational Models. - Heng Lian; Di Wu 056; Bo-Jian Hou; Jian Wu 006; Yi He 007 (IEEE Trans. Knowl. Data Eng., 2024)

Statistical performance of quantile tensor regression with convex regularization. - Wenqi Lu; Zhongyi Zhu; Rui Li; Heng Lian (J. Multivar. Anal., 2024)

Image classification based on tensor network DenseNet model. - Chunyang Zhu; Lei Wang; Weihua Zhao; Heng Lian (Appl. Intell., 2024)

Distributed Estimation of Support Vector Machines for Matrix Data. - Wangli Xu; Jiamin Liu; Heng Lian (IEEE Trans. Neural Networks Learn. Syst., 2024)

Metric-Agnostic Continual Learning for Sustainable Group Fairness. - Heng Lian; Chen Zhao; Zhong Chen 003; Xingquan Zhu 001; My T. Thai; Yi He 007 (AAAI, 2025)

Distributed iterative hard thresholding for variable selection in Tobit models. - Changxin Yang; Zhongyi Zhu; Hongmei Lin; Zengyan Fan; Heng Lian (Comput. Stat. Data Anal., 2025)

Optimal decorrelated score subsampling for Cox regression with massive survival data. - Yujing Shao; Zhaohan Hou; Lei Wang 118; Heng Lian (Neurocomputing, 2025)

Optimal Decorrelated Score Subsampling for High-Dimensional Generalized Linear Models Under Measurement Constraints. - Yujing Shao; Lei Wang 118; Heng Lian (J. Comput. Graph. Stat., 2025)

Optimal distributed subsampling for expected shortfall regression via Neyman-orthogonal score. - Xing Li; Lei Wang 118; Heng Lian (Knowl. Based Syst., 2025)

Sample efficient reinforcement learning via low-rank regularization. - Jiamin Liu; Heng Lian (Knowl. Based Syst., 2025)

Improved analysis of supervised learning in the RKHS with random features: Beyond least squares. - Jiamin Liu; Lei Wang; Heng Lian (Neural Networks, 2025)

Kernel-Based Regularized Learning with Random Projections: Beyond Least Squares. - Jiamin Liu; Junzhuo Gao; Heng Lian (SIAM J. Math. Data Sci., 2025)

Decentralized Nonconvex Low-rank Matrix Recovery. - Junzhuo Gao; Heng Lian (IEEE Trans. Image Process., 2025)

Optimal distributed subsampling under heterogeneity. - Yujing Shao; Lei Wang 118; Heng Lian (Stat. Comput., 2025)

Sparse and debiased Lasso estimation and statistical inference for long time series via divide-and-conquer. - Jin Liu; Wei Ma; Lei Wang; Heng Lian (Stat. Comput., 2025)

Debiased transfer learning estimation and inference for multinomial regression. - Jichen Yang; Lei Wang; Heng Lian (Stat. Comput., 2025)

Kernel-Based Decentralized Policy Evaluation for Reinforcement Learning. - Jiamin Liu; Heng Lian (IEEE Trans. Neural Networks Learn. Syst., 2025)

SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution. - Han Li; Yuling Shi; Shaoxin Lin; Xiaodong Gu; Heng Lian; Xin Wang; Yantao Jia; Tao Huang; Qianxiang Wang (CoRR, 2025)

SWE-Exp: Experience-Driven Software Issue Resolution. - Silin Chen; Shaoxin Lin; Xiaodong Gu; Yuling Shi; Heng Lian; Longfei Yun; Dong Chen; Weiguo Sun; Lin Cao; Qianxiang Wang (CoRR, 2025)

Sparse estimation and inference for prediction-powered semi-supervised linear regression. - Zihao Song; Jicai Liu; Lei Wang; Heng Lian; Weihua Zhao (Stat. Comput., 2025)

Communication-efficient transfer learning for adaptive Huber regression. - Jichen Yang; Lei Wang; Heng Lian (Stat. Comput., 2025)