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 Assistant 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

2019

[106], , and . What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), .
[105] and . On Constrained Open-World Probabilistic Databases, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), .
[104] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), .
[103], and . Generating and Sampling Orbits for Lifted Probabilistic Inference, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), .
[102], , and . Active Inductive Logic Programming for Code Search, In The 41st ACM/IEEE International Conference on Software Engineering (ICSE), .
[101], , , and . Scalable Rule Learning in Probabilistic Knowledge Bases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[100] and . On Constrained Open-World Probabilistic Databases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[99], and . The Institutional Life of Algorithms: Lessons from California's Money Bail Reform Act, In The 8th Annual Conference On Robotics, Law & Policy, .
[98] and . Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), . Oral full presentation, acceptance rate 460/7700 = 6%

2018

[97] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Advances in Neural Information Processing Systems 31 (NeurIPS), . Oral full presentation, acceptance rate 30/4856 = 0.6%

Recent Talks