UCLA StarAI Lab

Statistical and Relational Artificial Intelligence Lab

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

https://github.com/UCLA-StarAI
guyvdb@cs.ucla.edu
guy@sigmoid.social
@guyvdb

The Statistical and Relational Artificial Intelligence (StarAI) lab is directed by Prof. Guy Van den Broeck. The StarAI lab performs research on 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
[206]	Oliver Broadrick, Honghua Zhang and Guy Van den Broeck. Polynomial Semantics of Tractable Probabilistic Circuits, In Arxiv, 2024.
[205]	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.
[204]	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.
[203]	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.
[202]	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.
2023
[201]	Vinay Shukla, Zhe Zeng, Kareem Ahmed and Guy Van den Broeck. A Unified Approach to Count-Based Weakly Supervised Learning, In Advances in Neural Information Processing Systems 36 (NeurIPS), 2023.
[200]	Zhe Zeng and Guy Van den Broeck. Collapsed Inference for Bayesian Deep Learning, In Advances in Neural Information Processing Systems 36 (NeurIPS), 2023.
[199]	Kareem Ahmed, Kai-Wei Chang and Guy Van den Broeck. A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints, In Advances in Neural Information Processing Systems 36 (NeurIPS), 2023.
[198]	Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert and Christopher Morris. Probabilistic Task-Adaptive Graph Rewiring, In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, 2023.
[197]	William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd Millstein and Guy Van den Broeck. Scaling Integer Arithmetic in Probabilistic Programs, In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023.