王才兴，bw·西汉姆联助理教授、副研究员，毕业于上海财经大学必威betway西汉姆，2019 年和 2024 年分别获得统计学学士和博士学位，2024—2025 年在香港中文大学统计系从事博士后研究。主要研究方向包括统计机器学习、非参数方法、分位数回归等。曾主持上海财经大学研究生创新项目，多次参与国家自然科学基金青年和面上项目，在国际顶级或权威的统计与机器学习期刊及会议（包括 Journal of Machine Learning Research、Journal of Computational and Graphical Statistics、Statistics Probability Letters、NeurIPS、ICML 等）发表多篇学术论文。

欢迎数学基础扎实、编程能力强、肯努力的同学加入，一起探索统计与机器学习的前沿研究。

个人主页：wangcaixing96.com

主要研究方向包括统计机器学习、非参数方法、分位数回归等。

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.