题目：Machine Collaboration

主讲人：日本法政大学 刘庆丰教授

主持人：统计学院 常晋源教授

时间：2022年6月17日（周五）上午9:30-10:30

地点：腾讯会议，923 707 055

报告摘要：

We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of possibly heterogeneous base learning methods (hereafter, base machines) for prediction tasks. Unlike bagging/stacking (a parallel & independent framework) and boosting (a sequential & top-down framework), MaC is a type of circular & recursive learning framework. The circular & recursive nature helps the base machines to transfer information circularly and update their structures and parameters accordingly. The theoretical result on the risk bound of the estimator from MaC reveals that the circular & recursive feature can help MaC reduce risk via a parsimonious ensemble. We conduct extensive experiments on MaC using both simulated data and 119 benchmark real datasets. The results demonstrate that in most cases, MaC performs significantly better than several other state-of-the-art methods, including classification and regression trees, neural networks, stacking, and boosting.

主讲人简介：

刘庆丰，日本法政大学工业与系统工程系教授。2007年博士毕业于日本京都大学，研究领域为计量经济学、统计学和机器学习。现为Journal of the American Statistical Association、Journal of Econometrics、Econometric Theory等期刊的审稿人。在Journal of Business & Economic Statistics、Economics Letters、Econometric Reviews等期刊发表论文二十余篇，并于2017年取得国际管理工程师协会最佳论文奖。