Scalable inference on regularized Dirichlets

Hi all,

I'm giving a brief (30") talk about my recent work this Wednesday at noon in room 903 SSW. The abstract is below. I'll presumably be giving a fuller talk on the same eventually in our group meeting, but in case you're looking for lunchtime entertainment...

Cheers,
Carl

P.S. This is a one-hour situation with two half-hour presenters. So if you come, you could wind up watching someone else first, or only!


Title: Tractable inference on regularized Dirichlet distributions: a scalable class of HMM

Abstract: There is substantial interest in tractable inference on distributions of distributions, confined obviously to a simplex. Regularization of the Dirichlet distribution of random variables, without compromising tractability of inference, would be useful for encoding prior knowledge of interactions among the components, for instance in topic models. I will present a class of regularized Dirichlet distributions that are in fact especially scalable hidden Markov models. The same framework allows for tractable exact inference on certain loopy graphs of the same type.