报告人 ：朱冀
报告地点：数学与统计学院五楼科学报告厅（501室）
报告时间：2015年12月09日星期三09:00-10:00
主讲人简介：
朱冀，毕业于斯坦福大学，现为密歇根大学统计系教授，长期从事统计学，计算机科学，金融工程以及相关交叉学科的研究，其研究领域涉及统计机器学习，数据挖掘，高维数据，网络模型，金融，管理等各个方面，已在国际著名统计刊物上发表学术论文六十余篇。朱冀教授目前担任七家国际著名统计期刊的副主编，以及全美统计协会统计学习和数据挖掘分会会长。2008年获得美国国家科学基金的CAREER奖。
报告摘要：
The problem of estimating probabilities of network edges from the observed adjacency matrix has important applications to predicting missing links and network denoising. It has usually been addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but is ill-defined without strong assumptions on the network structure. Here we propose a novel computationally efficient method based on neighborhood smoothing to estimate the expectation of the adjacency matrix directly, without making the strong structural assumptions graphon estimation requires. The neighborhood smoothing method requires little tuning, has a competitive mean-squared error rate, and outperforms many benchmark methods on the task of link prediction in both simulated and real networks. This is joint work with Yuan Zhang and Elizaveta Levina.
