Date: 2019-02-01

Time: 15:30-16:30

Location: BURN 1205

Abstract:

A recent body of work, by myself and many others, aims to develop a statistical theory of network data for problems a single network is observed. Of the models studied in this area, graphon models are probably most widely known in statistics. I will explain the relationship between three aspects of this work: (1) Specific models, such as graphon models, graphex models, and edge-exchangeable graphs. (2) Sampling theory for networks, specifically in the case statisticians might refer to as an infinite-population limit. (3) Invariance properties, especially various forms of exchangeability. I will also present recent results that show how statistically relevant results (such as central limit theorems) can be derived from such invariance properties.

Speaker

Peter Orbanz is an Associate Professor in the Department of Statistics at Columbia University. He studies network and relational data, Bayesian nonparametrics, asymptotics and ergodicity of random structures, and hierarchies of latent variables. More generally, he is interested in all mathematical aspects of machine learning and artifical intelligence.

He was an undergraduate at the University of Bonn, a PhD student of Joachim M. Buhmann at ETH Zurich, and a postdoc of Zoubin Ghahramani at the University of Cambridge.