Title: The Sum-Product Theorem and its Applications

Advisor: Pedro Domingos

Supervisory Committee: Pedro Domingos (Chair), Jeffrey Bilmes (GSR, EE), Carlos Guestrin, and Henry Kautz (University of Rochester)


Models in artificial intelligence (AI) and machine learning (ML) must be expressive enough to accurately capture the state of the world, but tractable enough that reasoning and inference within them is feasible. However, many standard models are incapable of capturing sufficiently complex phenomena when constrained to be tractable. In this work, I study the cause of this inexpressiveness and its relationship to inference complexity. I use the resulting insights to develop more efficient and expressive models and algorithms for many problems in AI and ML, including nonconvex optimization, computer vision, and deep learning. 

CSE 305
Tuesday, December 12, 2017 - 14:00 to 16:00