Tractable Probabilistic Circuits


Guy Van den Broeck

University of California
Fri, May 27 2022 - 10:00 am (GMT + 7)
About Speaker

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 papers have been recognized with awards from key conferences such as AAAI, UAI, KR, OOPSLA, and ILP. Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.


Probabilistic circuits represent distributions through the computation graph of probabilistic inference, as a type of neural network. They move beyond probabilistic graphical models and other deep generative models by guaranteeing tractable inference for certain classes of queries: marginal probabilities, entropies, expectations, and related queries of interest. These probabilistic circuit models are now also effectively learned from data, outperforming VAE and flow-based likelihoods on MNIST-family benchmarks. They thus enable new solutions to some key problems in machine learning, including state-of-the-art neural compression results. This talk will overview these recent developments, in terms of learning, probabilistic inference, theory, and applications.

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