Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.
On Computational Abstractions of Probability Distributions
Probabilistic graphical models are a rich staple of probabilistic AI. However, they make a very specific choice of abstraction: probability distributions are represented by their variable-level (in)dependencies. In this talk I present some recent work on probabilistic models that go beyond classical PGMs, and make a radically different choice of abstraction; one that is computational. Concretely, I will discuss two classes of models: probabilistic circuits and probabilistic programs. Probabilistic circuits represent distributions through the computation graph of probabilistic inference. They move beyond PGMs by guaranteeing tractable inference for certain classes of queries. Probabilistic programs represent distributions through higher-level primitives of computation: iteration, branching, and procedural abstraction. They move beyond PGMs by looking “inside” of the dependencies. Finally, I will show how these two computational abstractions are themselves closely related, by showing how the Dice probabilistic programming language compiles probabilistic programs into probabilistic circuits for inference.