Dr. Xu’s research is in sparse modeling and statistical learning. His interests include feature selection, regularization methods, high-dimensional regression, kernel methods, and statistical computing. Recently, he focuses on developing efficient processing methods for big data, where traditional methods are less helpful due to the high computational burden. His works emphasize on both theoretical and computational aspects, which have a wide application scope in various disciplines.
Selected Publications
- Li, X. and Xu, C. (2023) Feature Screening with Conditional Rank Utility for Big-data Classification, Journal of the American Statistical Association, In press.
- Xu, C., Xu, W. and Jing, K. (2023) Fast Algorithms for Singular Value Decomposition and Inverse of Nearly Low-rank Matrices, National Science Review, In press.
- Yang, S., Zhang, L., Yu, H., Xu, C., Fan, J. and Xu, Z. (2023) Massive Data Clustering by Multi-scale Psychological Observations, National Science Review, 9(2): 1-9.
- Xia, Z., Chen, Y. and Xu, C. (2022) Multiview PCA: A Methodology of Feature Extraction and Dimension Reduction for High-order Data, IEEE Transactions on Cybernetics, 52(10): 11068-11080.
- Zhang, F., Wang, J., Wang, W. and Xu, C. (2021) Low-Tubal-Rank Plus Sparse Tensor Recovery with Prior Subspace Information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10): 3492-3507.
- Zhou, T., Zhu, L., Xu, C. and Li, R. (2020) Model-free Forward Regression via Cumulative Divergence, Journal of the American Statistical Association, 115(531): 1393-1405.
- Li, X., Li, R., Xia, Z. and Xu, C. (2020) Distributed Feature Screening via Component-wise Debiasing, Journal of Machine Learning Research, 21(24): 1-32.
- Wang, J., Xu, C., Yang, X. and Zurada, J. (2018) A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method, IEEE Transactions on Neural Networks and Learning Systems, 29(5): 2012-2024.
- Xu, C., Zhang, Y., Li, R. and Wu, X. (2016) On the Feasibility of Distributed Kernel Regression for Big Data, IEEE Transactions on Knowledge and Data Engineering, 28(11): 3041-3052.
- Xu, C. and Chen, J. (2014) The Sparse MLE for Ultra-high-dimensional Feature Screening, Journal of the American Statistical Association, 109(507): 1257-1269. R package
Editorial Service
- Associate Editor, Journal of American Statistical Association-T&M (2023 - present)
- Associate Editor, Electronic Journal of Statistics (2023 – present)
- Associate Editor, The Canadian Journal of Statistics (2019-2021)
Research Groups
- Statistics and Biostatistics;
- Data Science, Machine Learning, and Artificial Intelligence