You are all cordially invited to the AMLab seminar on Monday November 12 at 11:00 (note the unusual date and time!) in C3.163 (FNWI, Amsterdam Science Park), where Peter Orbanz (Columbia University) will give a talk titled “Statistical models of large graphs and networks”. Afterwards there are the usual drinks and snacks.
Abstract: Relational data is, roughly speaking, any form of data that can be represented as a graph: A social network, user preference data, protein-protein interactions, etc. A recent body of work, by myself and others, aims to develop a statistical theory of such data for problems where a single graph is observed (such as a small part of a large social network). Keywords include graphon, edge-exchangeable and sparse exchangeable graphs, and many latent variable models used in machine learning. I will summarize the main ideas and results of this theory: How and why the exchangeability assumptions implicit in commonly used models for such data may fail; what can be done about it; what we know about convergence; and implications of these results for methods popular in machine learning, such as graph embeddings and empirical risk minimization.
Bio: Peter Orbanz is associate professor of statistics at Columbia University. His research interests include network and relational data, Bayesian nonparametrics, symmetry principles in machine learning and statistics, and hierarchies of latent variables. He was an undergraduate student at the University of Bonn, a PhD student at ETH Zurich, and a postdoctoral fellow at the University of Cambridge.