Seminars

Spring 2013 GRASP Seminar: David Sontag, New York University, "Method-of-Moment Algorithms For Learning Bayesian Networks"

Presenter: David Sontag (Homepage)

Event Dates:
  Friday March 1, 2013 from 11:00am to 12:00pm

We present new algorithms for unsupervised learning of probabilistic topic models and noisy-OR Bayesian networks. Probabilistic topic models are frequently used to learn thematic structure from large document collections without human supervision, and the Bayesian networks that we study are often used for medical diagnosis. We circumvent the computational intractability of maximum likelihood learning by making the assumption that the observed data is drawn from a distribution within the model family that we are attempting to learn, such as Bayesian networks with latent variables. We demonstrate a set of structural constraints that make learning possible, yet are still realistic for many real-world applications. The new algorithms produce results comparable to the best MCMC implementations while running orders of magnitude faster.

Joint work with Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, Yichen Wu, and Michael Zhu


Presenter's Biography:

David Sontag is Assistant Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. His research interests include theoretical and practical aspects of machine learning and probabilistic inference. David’s recent work has focused on unsupervised learning of probabilistic models (e.g., for medical diagnosis) directly from clinical data found in electronic medical records. Prior to joining Courant, he was a postdoctoral researcher for Microsoft Research New England, 2010-11. David's Ph.D thesis won the award for the best doctoral thesis in Computer Science at MIT in 2010. His research has received recognition including a Best Paper Award at the conference on Empirical Methods in Natural Language Processing in 2010, a Best Paper Award at the conference on Uncertainty in Artificial Intelligence in 2008, and an Outstanding Student Paper Award at the conference on Neural Information Processing Systems in 2007.