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. (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.

• Wang, C., Shen, Z., Wang, S., Feng, X. Estimation and Inference on Distributed High-Dimensional Quantile Regression: Double-Smoothing and Debiasing. Submitted.