[talks] Andrej Risteski will present his pre-FPO on Tuesday, May 16th, 2017 at 1pm in CS 302

Nicki Gotsis ngotsis at CS.Princeton.EDU
Thu May 11 11:38:39 EDT 2017

Andrej Risteski will present his pre-FPO on Tuesday, May 16th, 2017 at 1pm in CS 302. 

The members of his committee are: 
Readers: Sanjeev Arora (advisor), Rong Ge (Duke University), Elad Hazan 
Non-readers: Bernard Chazelle, Mark Braverman 

The talk title and abstract follow below. All are welcome to attend. 

Title : New techniques for learning and inference in probabilistic graphical models 

Abstract : A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. 
The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the best fit graphical model from raw data, while inference is the task of answering probabilistic queries for a known graphical model (for example what is the marginal distribution of one of the variables or what is the distribution of a subset of variables, after conditioning on the values of some other subset of variables). Learning can be thought of as finding a graphical model that “explains” the raw data, while the inference queries extract the “knowledge” the graphical model contains. 

This talk will concern new provable techniques for performing both of these tasks. I will focus on a few vignettes: in the context of inference, I will talk about a new understanding of a class of algorithms for calculating partition functions, called variational methods through a combination of convex programming hierarchies — a recent area of interest in the area of combinatorial optimization — and entropy approximations based on low-order moments of a distribution. In the context of learning, I will talk about alternating-minimization and method-of-moments techniques for learning noisy-or Bayesian networks and topic modeling. The former are used for modeling the causal structure of diseases and symptoms, and the later for modeling the latent structure of text. 

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