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

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• Peter Orbanz (Columbia University)
• Thursday 20 October 2016, 14:00-15:00
• Seminar Room 2, Newton Institute.

If you have a question about this talk, please contact INI IT.

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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