Office: 613-562-5800 ext. 2030
Work E-mail: cx3@uOttawa.ca
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 such as genetics, biology, health science, geology, finance, and internet studies.
- Wang, J., Xu, C., Yang, X. and Zurada, J. (2017). A Novel Pruning Algorithm for Smoothing Feed-forward Neural Networks based on Group Lasso. IEEE Transactions on Neural Networks and Learning Systems. Accepted.
- 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, 3041-3052.
- Xu, C., Lin, S., Fang, J. and Li, R. (2016). Prediction-based Termination Rule for Greedy Learning with Massive Data. Statistica Sinica, 26, 841-860.
- Xu, C. and Chen, J. (2014). The Sparse MLE for Ultra-high-dimensional Feature Screening. Journal of the American Statistical Association, 109, 1257-1269.
- Xu, C., Chen, J. and Mantel, H. (2013). Pseudo-likelihood-based Bayesian Information Criterion in Analysis of Survey Data. Survey Methodology, 39, 303-321.
Research Group(s): Statistics and Probability