Random Forests and Deep Neural Networks for Euclidean and Non-Euclidean regression

发布时间：2024-05-23 作者：浏览次数：

Speaker:	於州	DateTime:	2024年05月24日（周五）上午09:00-10:00
Brief Introduction to Speaker: 於州，华东师范大学教授、博士生导师，统计学院副院长。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, Journal of the American Statistical Association等知名统计期刊上发表论文40余篇。曾主持国家自然科学基金青年、面上项目，获得第十届国家统计局统计科研成果二等奖，上海市自然科学二等奖，霍英东教育基金会高等院校青年科学奖二等奖。并先后入选上海市青年科技启明星、上海高校东方学者特聘教授、上海市青年拔尖人才，国家青年人才计划。		Place:	6号楼二楼报告厅
Abstract: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.

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