Guy Van den Broeck

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596
+1 (310) 206-6552
Pronounce my name
guyvdb@cs.ucla.edu  

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

2020

[137], and . Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, .  [doi]
[136], and . Probabilistic Circuits: A Unifying Framework for Tractable Probabilistic Models, In , .
[135], , , and . Relax, compensate and then integrate, In Proceedings of the ECML-PKDD Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), .
[134], and . Strudel: Learning Structured-Decomposable Probabilistic Circuits, In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM), .
[133], and . On the Relationship Between Probabilistic Circuits and Determinantal Point Processes, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[132] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[131], and . Towards Probabilistic Sufficient Explanations, In Extending Explainable AI Beyond Deep Models and Classifiers Workshop at ICML (XXAI), .
[130], , , and . Handling Missing Data in Decision Trees: A Probabilistic Approach, In The Art of Learning with Missing Values Workshop at ICML (Artemiss), .
[129], , , and . Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing, In Proceedings of the 37th International Conference on Machine Learning (ICML), .
[128], , , , , , , and . Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits, In Proceedings of the 37th International Conference on Machine Learning (ICML), .

Recent Talks

TutorialMay 2020
Slides

Probabilistic Circuits: Inference, Representations, Learning and Theory

UCLA Computer Science Department - CS201 Seminar