Guy Van den Broeck

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596
+1 (310) 206-6552
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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 (Statistical Relational Learning, Tractable Learning), Knowledge Representation and Reasoning (Graphical Models, Lifted Probabilistic Inference, Knowledge Compilation), Applications of Probabilistic Reasoning and Learning (Probabilistic Programming, Probabilistic Databases), and Artificial Intelligence in general.

Recent Publications


[169], , , and . Tractable and expressive generative models of genetic variation data, In International Conference on Research in Computational Molecular Biology (RECOMB), .
[168], and . Lossless Compression with Probabilistic Circuits, In International Conference on Learning Representations (ICLR), . Oral spotlight presentation, acceptance rate 176/3391 = 5.2%
[167], and . Solving Marginal MAP Exactly by Probabilistic Circuit Transformations, In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), .
[166], , , , , , , and . PYLON: A PyTorch Framework for Learning with Constraints, In Proceedings of the 36th AAAI Conference on Artificial Intelligence (Demo Track), .
[165], and . Strudel: A Fast and Accurate Learner of Structured-Decomposable Probabilistic Circuits, In International Journal of Approximate Reasoning, volume 140, .  [doi]


[164] and . Tractable Regularization of Probabilistic Circuits, In Advances in Neural Information Processing Systems 35 (NeurIPS), . Oral spotlight presentation, acceptance rate 340/9122 = 3.7%
[163], , , and . A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference, In Advances in Neural Information Processing Systems 35 (NeurIPS), . Oral full presentation, acceptance rate 55/9122 = 0.6%
[162], , and . Neuro-Symbolic Entropy Regularization, .
[161], , and . flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs, In International Conference on Probabilistic Programming (PROBPROG), .
[160], , , , and . Towards an Interpretable Latent Space in Structured Models for Video Prediction, In IJCAI 2021 Weakly Supervised Representation Learning Workshop (WSRL), .

Recent Talks