学术报告：Random Forests and Deep Neural Networks for Euclidean and Non-Euclidean regression

报告时间：12月13日（星期五）下午14:30-15:30

报告地点：沙河校区，学院1号楼102会议室

报告人：於州，华东师范大学统计学院，教授

报告摘要：Neural networks and random forests are popular and promising tools for machine learning. We explore the proper integration of these two approaches for nonparametric regression to improve the performance of a single approach. It naturally synthesizes the local relation adaptivity of random forests and the strong global approximation ability of neural networks.. By utilizing advanced U-process theory and an appropriate network structure, we obtain the minimax convergence rate for the estimator. Moreover, we propose the novel random forest weighted local Frechet regression paradigm for regression with Non-Euclidean responses. We establish the consistency, rate of convergence, and asymptotic normality for the Non-Euclidean random forests based estimator.

报告人简介：於州，华东师范大学教授、博士生导师。主要研究方向为高维数据统计分析及统计机器学习，在Annals of Statistics, Biometrika，JASA, JRSSB, Journal of Machine Learning Research, IEEE Information Theory等知名统计及机器学习期刊上发表论文50余篇。曾主持国家重点研发计划课题、自然科学基金青年、面上项目，获得上海市自然科学二等奖等奖项，霍英东青年科学奖二等奖。并先后入选上海高校东方学者特聘教授，上海市青年拔尖人才，国家级青年人才等计划。

撰稿人：刘洁

审稿人：邓露