团队负责人：於州
研究目标：
面向国家和地方发展重大战略需求，立足于统计学数理方法,开展机器学习前沿领域的理论和应用研究。聚焦深度学习和强化学习的理论算法基础、多源数据融合建模、网络和图结构数据分析等领域实现在智能交通、智慧医疗、大数据征信、航空航天等领域的应用。
代表性论文：
[1] Chen, YinFeng; Jiao, YuLing; Qiu, Rui; Yu, Zhou. Deep Nonlinear SufficientDimension Reduction. To appear in Annals of Statistics. 2024, 52(3): 1201-1226
[2] Qiu, Rui; Xu, Shuntuo; Yu, Zhou, Neural Networks Meet Random Forests. Journalof the Royal Statistical Society Series B: Statistical Methodology. 2024, 86(5):1435-1454.
[3] Qiu, Rui; Yu, Zhou; Zhu, Ruoging. Random Forest Weighted Local FréchetRegression with Random Objects. Journal ofMachine Learning Research. 202425(107):1-69.
[4] Ying, Chao; Yu, Zhou, Frechet sufficient dimension reduction for random objects. Biometrika. 2022,109(4):975-992.
[5] Wang, Yudong; Tang, Yanlin; Ye, Zhi-Sheng.Paired or partially paired two-sampletests with unordered samples. Journal of the Royal Statistical Society Series B:Statistical Methodology. 2022, 84(4): 1503-1525