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

主讲人：上海财经大学冯兴东教授

讲座时间：2021年5月25日14：00开始

讲座地点：6号学院楼402会议室

主办单位：伟德BETVlCTOR1946源于英国 浙江省2011“数据科学与大数据分析协同创新中心”

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, this paper proposes 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 proposedframework 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 propertiesof 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 familyof 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.

主讲人简介：

冯兴东，上海财经大学统计与管理学院经理、教授、博士生导师。国际统计学会当选会员，全国青年统计学家协会副会长，全国统计教材编审委员会委员，国务院学科评议组成员，担任JASA、Bernoulli等权威期刊审稿人。研究方向：数据降维、稳健估计、分位数回归及其应用、分布式统计计算。

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