Heng Lian

18 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0002-6008-6614

City University of Hong Kong Shenzhen Research Institute, Shenzhen, China;c Department of Mathematics, City University of Hong Kong, Hong Kong, China

Publications

• Kernel-based Distributed Learning — Journal of Machine Learning Research 2026 (name-matched, not ORCID-linked)
• Statistical Inference for High-Dimensional Convoluted Rank Regression — Journal of the American Statistical Association 2025
• Local Signal Detection on Irregular Domains with Generalized Varying Coefficient Models — Journal of the American Statistical Association 2025
• Optimal subsampling for high-dimensional partially linear models via machine learning methods — Journal of Machine Learning Research 2025 (name-matched, not ORCID-linked)
• More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization — Journal of Machine Learning Research 2024 (name-matched, not ORCID-linked)
• Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis — Journal of the American Statistical Association 2023
• Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
• Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
• High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition — Journal of the American Statistical Association 2022
• Ultra-High Dimensional Single-Index Quantile Regression — Journal of Machine Learning Research 2020 (name-matched, not ORCID-linked)
• High-dimensional quantile tensor regression — Journal of Machine Learning Research 2020 (name-matched, not ORCID-linked)
• Projected spline estimation of the nonparametric function in high-dimensional partially linear models for massive data — The Annals of Statistics 2019
• Dimensionality Reduction and Variable Selection in Multivariate Varying-Coefficient Models With a Large Number of Covariates — Journal of the American Statistical Association 2018
• Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space — The Annals of Statistics 2018
• Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions — Journal of Machine Learning Research 2017 (name-matched, not ORCID-linked)
• Variance function partially linear single-index models — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2015
• Letter to the Editor — The Annals of Applied Statistics 2013
• Kernel-based Adaptive Huber Mean Regression with Heavy Tails and Contamination — Journal of the American Statistical Association