Guy Van den Broeck

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596
+1 (310) 206-6552
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

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2018

[98] and . Learning Logistic Circuits, In Proceedings of the UAI 2018 Workshop: Uncertainty in Deep Learning, . [bibtex]
[97] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), . [bibtex] [pdf]
[96], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), . [bibtex] [pdf]
[95] and . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), . [bibtex] [pdf]
[94], and . Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), . [bibtex] [pdf]
[93], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), . [bibtex] [pdf]
[92] and . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), . [bibtex] [pdf]
[91] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In ArXiv e-prints, . [bibtex] [pdf]
[90], and . Probabilistic Program Inference With Abstractions, In POPL 2018 Probabilistic Programming Languages, Semantics, and Systems Workshop, . [bibtex] [pdf]

2017

[89], , , and . A Semantic Loss Function for Deep Learning Under Weak Supervision, In NIPS 2017 Workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond, . [bibtex] [pdf]
LLD best paper award runner up