Journal ($\star$ denotes to corresponding author, $\dagger$ denotes to equal contribution or Alphabet ordering, # denotes to my students)

• Wang, C., Heng, Q., Kuang, Q., Feng, X. “Improved Statistical Analysis for Spectral Algorithms under General Conditions: Optimality and Robustness”. Minor Revision Resubmitted to Journal of American Statistical Association.

• Shen, Z.^{$^\dagger$}, Wang, C. ^{$^\dagger$}, Wang, S.^{$^\dagger$}, Yan, Y. ^{$^\dagger$} “High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference”. Minor Revision Resubmitted to IEEE Transactions on Information Theory.

• Wang, C.^{$^\dagger$}, Wang, C. ^{$^\dagger$}, He, X., Feng, X (2026+). “Transfer Learning for Kernel-based Regression”. Journal of American Statistical Association, accepted.

• Wang, C. (2026). “Generalization Properties of Robust Learning with Random Features”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 48 (7), 8199-8215.

• Chen, F.^{$^\dagger$}, Wang, C. ^{$^\dagger$$^\star$} (2025+). “Estimation of Conditional Extremiles in Reproducing Kernel Hilbert Spaces with Application to Large Commercial Banks Data”. Statistica Sinica, accepted.

• Feng, X.^{$^\dagger$}, He, X.^{$^\dagger$}, Jiao, Y.^{$^\dagger$}, Kang, L.^{$^\dagger$}, 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. (2024). “Communication-efficient Nonparametric Quantile Regression via Random Features”. Journal of Computational and Graphical Statistics 33 (4), 1175–1184.

• Feng, X., 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. (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., Wang, Y. “ Random Projections for Spectral Algorithms: Optimal Rates in Sobolev Norms”. Major revision in Artificial Intelligence Journal.

• Zhang, Y.^{$^\dagger$}, Luo, J^{$^\dagger$}, Wang, C. ^{$^\star$} “Enhancing Fairness and Efficiency in Image Classification via Weighted SVD-Based Fine-Tuning of Large Pre-trained Models”. Revision Resubmitted to Annals of Applied Statistics.

• Heng, Q., Wang, C ^{$^\star$}. “Inertial Quadratic Majorization Minimization with Application to Kernel Regularized Learning”. Under review.

• Wang, C., Chen, Z.^#, Wang, Y. “Adaptive Regularization for Random Features: a Neighboring Early-stopping Rule with Oracle-rate Guarantees”. Under review.

• Wang, C. “Adversarial Learning with Spectral Regularization: A Unified Analysis of Generalization and Robustness”. Under review.