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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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

[114], , 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), .
[113] and . On Constrained Open-World Probabilistic Databases, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), .
[112] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), .
[111], and . Generating and Sampling Orbits for Lifted Probabilistic Inference, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), .
[110], , , and . Tractable Computation of the Moments of Predictive Models, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[109], and . Symbolic Exact Inference for Discrete Probabilistic Programs, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[108], , and . Towards Hardware-Aware Tractable Learning of Probabilistic Models, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[107], , and . Smoothing Structured Decomposable Circuits, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[106], , and . Active Inductive Logic Programming for Code Search, In The 41st ACM/IEEE International Conference on Software Engineering (ICSE), .
[105], , , and . Scalable Rule Learning in Probabilistic Knowledge Bases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .

Recent Talks