Guy Van den Broeck

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596

Pronounce my name
guyvdb@cs.ucla.edu
guy@sigmoid.social
@guyvdb

I am an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where I direct the Statistical and Relational Artificial Intelligence (StarAI) lab. My research interests are in Machine Learning (Tractable Deep Generative Models, Statistical Relational Learning, Probabilistic Programming), Knowledge Representation and Reasoning (Probabilistic Inference, Probabilistic Databases), and Artificial Intelligence in general.

Recent Publications

2024
[205]	Anji Liu, Kareem Ahmed and Guy Van den Broeck. Scaling Tractable Probabilistic Circuits: A Systems Perspective, In Proceedings of the 41th International Conference on Machine Learning (ICML), 2024.
[204]	Vinh Tong, Anji Liu, Trung-Dung Hoang, Guy Van den Broeck and Mathias Niepert. Learning to Discretize Denoising Diffusion ODEs, In Arxiv, 2024.
[203]	Xuejie Liu, Anji Liu, Guy Van den Broeck and Yitao Liang. A Tractable Inference Perspective of Offline RL, In Arxiv, 2024.
[202]	Siyan Zhao, Daniel Israel, Guy Van den Broeck and Aditya Grover. Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models, In Arxiv, 2024.
[201]	Antoine Amarilli, Marcelo Arenas, YooJung Choi, Mikaël Monet, Guy Van den Broeck and Benjie Wang. A Circus of Circuits: Connections Between Decision Diagrams, Circuits, and Automata, In Arxiv, 2024.
[200]	Oliver Broadrick, Honghua Zhang and Guy Van den Broeck. Polynomial Semantics of Tractable Probabilistic Circuits, In Proceedings of the 40th Conference on Uncertainty in Aritifical Intelligence (UAI), 2024.
[199]	Anji Liu, Mathias Niepert and Guy Van den Broeck. Image Inpainting via Tractable Steering of Diffusion Models, In Proceedings of the Twelfth International Conference on Learning Representations (ICLR), 2024.
[198]	Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert and Christopher Morris. Probabilistically Rewired Message-Passing Neural Networks, In Proceedings of the Twelfth International Conference on Learning Representations (ICLR), 2024.
[197]	Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt and Vincent Fortuin. On the Challenges and Opportunities in Generative AI, In Arxiv, 2024.
[196]	Poorva Garg, Steven Holtzen, Guy Van den Broeck and Todd Millstein. Bit Blasting Probabilistic Programs, In , Association for Computing Machinery, 2024.