一、报告题目：

Graph-based semi-supervised learning with nonignorable nonresponses

二、报告人：

周帆，上海财经大学统计与管理学院、助理教授

三、报告时间：

2019年11月18日 （周一） 下午14:00-15:30

四、报告地点：

知新楼B219

五、报告人简介：

周帆博士，美国北卡罗纳大学教堂山分校统计学博士毕业，师从朱宏图教授，现为上海财经大学统计与管理学院常任轨助理教授。周帆博士在Nature Genetics, Biometrics, NeuroImage, Bioinformatics等国际著名杂志发表了多篇相关学术论文。周帆博士的主要研究兴趣为传统统计学方法在深度学习和人工智能上的应用，相关成果被顶级国际人工智能和计算机视觉会议NeurIPS, MICCAI, ISBI接收。此外，周帆博士还担任多家国内著名科技公司包括滴滴出行等的学术顾问，并获得了ICSA（泛华统计协会）New Researcher Awards, NeurIPS travel award等国际奖项。

六、 报告摘要：

Graph-based semi-supervised learning is very important for many classification tasks, but most existing methods assume that all labelled nodes are randomly sampled. With the presence of nonignorable nonresponse, ignoring all missing nodes can lead to significant estimation bias and handicap the classifiers. To solve this issue, we propose a Graph-based joint model with Nonignorable Missingness (GNM) and develop an imputation and inverse probability weighting estimation approach. We further use graphical neural networks to model nonlinear link functions and then use a gradient descent (GD) algorithm to estimate all the parameters of GNM. We prove the identifiability of the GNM model and validate their predictive performance in both simulations and real data analysis through comparing with models ignoring or misspecifying the missingness mechanism.

七、主办单位：

565net必赢最新版