Basic Information
Caixing Wang is an Assistant Professor and Associate Research Fellow at Southeast University. He graduated from the School of Statistics and Data Science at Shanghai University of Finance and Economics, receiving his Bachelor's degree in Statistics in 2019 and Ph.D. degree in 2024. From 2024 to 2025, he conducted postdoctoral research in the Department of Statistics at The Chinese University of Hong Kong. His main research interests include statistical machine learning, nonparametric methods, and quantile regression. He has served as Principal Investigator for graduate innovation projects at Shanghai University of Finance and Economics and participated in multiple National Natural Science Foundation of China projects (both Youth and General Programs). He has published several academic papers in top-tier international statistics and machine learning journals and conferences, including Journal of Machine Learning Research, Journal of Computational and Graphical Statistics, Statistics & Probability Letters, NeurIPS, ICML, among others.
Students with solid mathematical foundations, strong programming skills, and a diligent work ethic are welcome to join us in exploring cutting-edge research at the intersection of statistics and machine learning.
Personal Homepage: wangcaixing96.com
Research Interests
Main research interests include statistical machine learning, nonparametric methods, and quantile regression.
Representative Publications
Journal Papers (∗ and † refer to corresponding author and equal contributions (or Alphabet ordering))
1. Xingdong Feng†, Xin He*†, Yuling Jiao†, Lican Kang*†, Caixing Wang†. Deep nonparametric quantile regression under covariate shift. Journal of Machine Learning Research 25 (385), 1-50.
2. Caixing Wang, Tao Li, Xinyi Zhang, Xingdong Feng, Xin He*. Communication efficient nonparametric quantile regression via random features. Journal of Computational and Graphical Statistics, 2024, 33(4), 1175–1184.
3. Xingdong Feng*, Qiaochu Liu, Caixing Wang. Statistics & Probability Letters 192, 109680, 2023.
Conference Papers
1. Caixing Wang, Xingdong Feng*. Optimal kernel quantile learning with random features. International Conference on Machine Learning, 2024, 235: 50419-50452, spotlight.
2. Caixing Wang*, Ziliang Shen. Distributed high-dimensional quantile regression: estimation efficiency and support recovery. International Conference on Machine Learning , 2024, 235: 51415-51441, spotlight.
3. Chao Wang, Xin He*, Xin Bing, Caixing Wang∗. Towards theoretical understanding of learning large-scale dependent data via random features. International Conference on Machine Learning, 2024, 235: 50118-50142, spotlight.
4. Xingdong Feng†, Xin He†, Caixing Wang∗†, Chao Wang†, Jingnan Zhang†. Towards a unified analysis of kernel-based methods under covariate shift. Advances in Neural Information Processing Systems, 2023, 36: 73839-73851, poster.
Preprints
1. Chao Wang†, Caixing Wang†, Xin He, Xingdong Feng. Transfer Learning for Kernel-based Regression. Journal of American Statistical Association, major revision.
2. Caixing Wang, Ziliang Shen, Shaoli Wang, Xingdong Feng. Estimation and inference on distributed high-dimensional quantile regression: double-smoothing and debiasing. Under review.
3. Qiang Heng, Caixing Wang*. Inertial quadratic majorization minimization with application to kernel regularized learning. Under review.
4. Fang Chen, Caixing Wang*. Estimation of conditional extremiles in reproducing kernel Hilbert spaces with application to large commercial banks data. Under review.
5. Ziliang Shen†, Caixing Wang†, Shaoli Wang†, Yibo Yan†. High-dimensional differentially private quantile regression: distributed estimation and statistical inference. Under review.
