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The role of invariance in learning from random graphs and structured data

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SNA - Theoretical foundations for statistical network analysis

Graphon models can be derived from the concept of exchangeability, which has long played an important role in (Bayesian) statistics. Exchangeability is, in turn, a special case of probabilistic invariance, or symmetry. This talk will be an attempt to explain, in as non-technical a manner as possible, why and how invariance is useful in statistics. I will cover some general results, discuss how different notions of exchangeability fit into the picture, and how invariance can be regarded as a consequence of assumptions on the process by which the data was sampled. All of this ultimately concerns the problem: What can we learn about an infinite random structure if only a finite sample from a single realization is observed?


This talk is part of the Isaac Newton Institute Seminar Series series.

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Room SS03, Computer Laboratory, William Gates Building Ryle seminar room + online. MAB Boulind Rooms 6&7 Mary Allan Building, Homerton College, Hills Road, Cambridge CB2 8PQ

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