Look Who it Was Random video

Archive: Learning Latent Variable Models: Overlapping Community and Overcomplete Models

4,362 views · Published 21 February 2014 · 57:17 · Indexed 20 October 2026

Channel: UW Video · 2014 · Education

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Incorporating latent or hidden variables is a crucial aspect of statistical modeling. I will present a statistical and a computational framework for guaranteed learning of a wide range of latent variable models. I will focus on two instances, viz., community detection and overcomplete representations.
The goal of community detection is to discover hidden communities from graph data. I will present a tensor decomposition approach for learning probabilistic mixed membership models. The tensor approach is guaranteed to correctly recover the mixed membership communities with tight guarantees. We have deployed it on many real-world networks, e.g. Facebook, Yelp and DBLP. It is easily parallelizable, and is orders of magnitude faster than the state-of-art stochastic variational approach.
I will then discuss recent results on learning overcomplete latent representations, where the latent dimensionality can far exceed the observed dimensionality. I will present two frameworks, viz., sparse coding and sparse topic modeling. Identifiability and efficient learning are established under some natural conditions such as incoherent dictionaries or persistent topics.


Animashree Anandkumar 
Cornell/UC Irvine

02/06/2014
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