报告主题：Random Forests and Deep Neural Networks for Euclidean and Non-Euclidean regression
时间：2024年10月30日14：20-15：00
地点：上川路校区一教218
报告人：於州
报告内容简介：
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.
主讲人简介：
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, 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，国家青年人才等计划。