题目：From CART to RF and then to QRF: Some Recent Research Brief
主讲人：虞克明
讲座时间：2026年7月12日(周日) 09：00-10：00
地点：综合楼644会议室
主讲人简介：虞克明，英国伦敦布鲁内尔大学统计学与数据科学讲习教授(Chair Professor)、数学学科研究影响中心主任；英国皇家统计学会会士、英国社科基金(ESRC) 评审专家成员、英国自科基金(EPSRC)评审专家成员、欧洲科学基金(ESF) 评审专家成员。目前是《Journal of the Royal Statistical Society-C》的编委，也担任过《Journal of the American Statistical Association, A&CS》、《Journal of the Royal Statistical Society-A》等多家国际SCI、SSCI期刊的编委。他连续入选2021、2022、2023及2025年斯坦福大学/爱思唯尔单年引用影响力排名全球前2%科学家名单。2024年，他被ScholarGPS评为高排名学者。
2026年，他在Research.com评选的全球顶尖数学家排名中位列英国第196位、全球第3054位。虞教授是国际公认的贝叶斯分位数回归先驱，在统计方法论与数据科学领域具有深远影响。先后在《Journal of American Statistical Association》、《Journal of the Royal Statistical Society: Series B》、《Journal of Econometrics》、《Journal of Business & Economic Statistics》、《Bernoulli》等统计学顶级刊物上发表论文150多篇。
摘要：The evolution from CART to Random Forest and then to Quantile Random Forest represents a natural progression in tree-based machine learning. CART provides an interpretable framework for modeling nonlinear relationships but is susceptible to overfitting. Random Forest overcomes this limitation by aggregating many randomized trees, greatly improving predictive accuracy and robustness. Quantile Random Forest further extends the Random Forest framework by estimating the entire conditional distribution of the response variable, enabling prediction intervals and uncertainty quantification. As a result, QRF has become a powerful tool for applications such as renewable energy forecasting, hydrology, environmental science, finance, and medical risk prediction, where understanding uncertainty is as important as obtaining accurate predictions. This talk briefs some of our recent research.
