CHINA·77779193永利(集团)有限公司-Official website

题目：Structure learning via unstructured kernel-based M-regression

主讲人：贺莘 （上海财经大学）

时间： 2021年10月15日 周五晚上  19：30-20：30

报告形式： 线上腾讯会议（会议号： 424 761 336）

摘要：In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis. Unlike most of those existing methods that focus on some specific settings under certain model assumptions, we propose a general and novel framework for recovering true structures of target functions by using unstructured M-regression in a reproducing kernel Hilbert space (RKHS).  The proposed framework is inspired by the fact that gradient functions can be employed as a valid tool to learn underlying structures, including sparse learning, interaction selection and model identification, and it is easy to implement by taking advantage of the nice properties of the RKHS.  More importantly, it admits a wide range of loss functions, and thus includes many commonly used methods, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification, which is also computationally efficient by solving convex optimization tasks. The asymptotic results of the proposed framework are established within a rich family of loss functions without any explicit model specifications.  The superior performance of the proposed framework is also demonstrated by a variety of simulated examples and a real case study.

报告人简介：贺莘，上海财经大学统计与管理学院, 副教授，博士生导师。2018年在香港城市大学取得博士学位。主要研究方向为统计机器学习及其在经济金融中的应用，研究成果发表在 Journal of the American Statistical Association、Electronic Journal of Statistics、Statistica Sinica、Annals of the Institute of Statistical Mathematics等国际期刊上。主持自然科学青年基金一项以及上海市浦江人才计划一项。

举办单位：首都师范大学77779193永利官网、交叉科学研究院

                北京应用统计学会

                北京国家应用数学中心

联系人：崔恒建