We are happy to share with you a recent talk by Sham Kakade from Microsoft recorded at the NYC Machine Learning meetup . In this talk he discusses a general and (computationally and statistically) efficient parameter estimation method for a wide class of latent variable models—including Gaussian mixture models, hidden Markov models and latent Dirichlet allocation—by exploiting a certain tensor structure in their low-order observable moments.
Dr. Sham Kakade is a senior research scientist at Microsoft Research, New England, a relatively new lab in Cambridge, MA. Previously, Dr. Kakade was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania (from 2010-2012) and was an assistant professor at the Toyota Technological Institute at Chicago. Before this, he did a postdoc in the Computer and Information Science department at the University of Pennsylvania under the supervision of Michael Kearns. Dr. Kakade completed his PhD at the Gatsby Unit where his advisor was Peter Dayan. Before Gatsby, Dr. Kakade was an undergraduate at Caltech where he did his BS in physics.