Publications
Journal ($\star$ denotes to corresponding author and $\dagger$ denotes to equal contribution (or Alphabet ordering))
Feng, X.$^\dagger$, He, X.$^\dagger$$^\star$, Jiao, Y.$^\dagger$, Kang, L.$^\dagger$$^\star$, Wang, C. $^\dagger$ (2024). “Deep Nonparametric Quantile Regression Under Covariate Shift”. Journal of Machine Learning Research 25 (385), 1-50.
Wang, C., Li, T., Zhang, X., Feng, X., He, X$^\star$. (2024). “Communication-efficient Nonparametric Quantile Regression via Random Features”. Journal of Computational and Graphical Statistics 33 (4), 1175–1184.
Feng, X$^\star$., Liu, Q., Wang, C. (2023). “A Lack-of-fit Test for Quantile Regression Process Models”. Statistics & Probability Letters 192, 109680.
Conference
Wang, C.$^\star$, Shen, Z. (2024). “Distributed High-dimensional Quantile Regression: Estimation Efficiency and Support Recovery”. International Conference on Machine Learning (Spotlight) 235, 51415-51441.
Wang, C., Feng, X$^\star$. (2024). “Optimal Kernel Quantile Learning with Random Features”. International Conference on Machine Learning (Spotlight) 235, 50419-50452.
Wang, C., Bing, X., He, X.$^\star$, Wang, C.$^\star$ (2024). “Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features”. International Conference on Machine Learning (Spotlight) 235, 50118-50142.
Feng, X.$^\dagger$, He, X.$^\dagger$, Wang, C. $^\dagger$$^\star$, Wang, C.$^\dagger$, Zhang, J.$^\dagger$ (2023). “Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift”. Advances in Neural Information Processing Systems 36, 73839-73851.
Preprints
Wang, C.$^\dagger$, Wang, C. $^\dagger$, He, X., Feng, X. “Transfer Learning for Kernel-based Regression”. Under Major Revision in Journal of American Statistical Association.
Chen, F., Wang, C. $^\star$ “Estimation of Conditional Extremiles in Reproducing Kernel Hilbert Spaces with Application to Large Commercial Banks Data”. Under Major Revision in Statistica Sinica.
Wang, C., Shen, Z., Wang, S., Feng, X. “Estimation and Inference on Distributed High-Dimensional Quantile Regression: Double-Smoothing and Debiasing”. Submitted.
Wang, C., Heng, Q., Kuang, Q., Feng, X. “Improved Statistical Analysis for Spectral Algorithms under General Conditions: Optimality and Robustness”. Submitted.
Heng, Q., Wang, C.$^\star$ “Inertial Quadratic Majorization Minimization with Application to Kernel Regularized Learning”. Submitted.
Shen, Z.$^\dagger$, Wang, C. $^\dagger$, Wang, S.$^\dagger$, Yan, Y. $^\dagger$$^\star$ “High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference”. Submitted.
