MATH Seminar

Title: Hierarchy and Structure: Nonparametric models for space, language, and relations
Seminar: Computer Science
Speaker: Alex Smola of Google/CMU
Contact: Eugene Agichtein, eugene@emory.edu
Date: 2014-01-09 at 3:00PM
Venue: MSC W301
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Abstract: Latent variable models are a powerful tool for analyzing structured data. They are well suited to capture documents, location and preference information. That said, often a simple hierarchical model is insufficient for modeling observations since real data tends to be more nuanced in some aspects rather than others. In other words, descriptions work best if they allow for variable depth and refined descriptions. Models such as the nested Chinese Restaurant Franchise address these issues. I will present examples of their application to location inference for Twitter and structured recommender systems.