報告題目：Random Forests and Deep Neural Networks for Euclidean and Non-Euclidean regression
報 告 人：於州 教授 華東師範大學
報告時間：2023年6月21日 15：00-16：00
報告地點：伟德线上平台第一報告廳
校内聯系人：韓月才 hanyc@jlu.edu.cn
報告摘要：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.
報告人簡介： 於州，華東師範大學教授、博士生導師、華東師範大學經濟與管理學部副主任，統計學院副院長。主要研究方向為高維數據統計分析及統計機器學習，在Annals of Statistics, Biometrika, Journal of the American Statistical Association等知名統計期刊上發表論文40餘篇。曾主持國家自然科學基金青年、面上項目，獲得第十屆國家統計局統計科研成果二等獎，上海市自然科學二等獎，霍英東教育基金會高等院校青年科學獎二等獎。并先後入選上海市青年科技啟明星、上海高校東方學者特聘教授、上海市青年拔尖人才，國家青年人才計劃。
