Faculty host Elena Erosheva
Columbia University - Electrical Engineering
Advances in scalable machine learning have made it possible to learn highly structured models on large data sets. In this talk, I will discuss some of our recent work in this direction. I will first briefly review scalable probabilistic topic modeling with stochastic variational inference. I will then then discuss two structured developments of the LDA model in the form of tree-structured topic models and graph-structured topic models. I will present our recent work in each of these areas.
Bio: John Paisley is an assistant professor of Electrical Engineering at Columbia University and is also a member of the Data Science Institute at Columbia. Prior to joining Columbia in 2013, he was a postdoctoral researcher in the Computer Science departments at Princeton University and at the University of California, Berkeley. He received the BSEE
(2004) and PhD (2010) degrees in Electrical & Computer Engineering from Duke University. His research interests revolve around probabilistic models for machine learning applications.