Guy Van den Broeck

UCLA - Computer Science Department
4531E Boelter Hall
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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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
[88], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In CoRR, volume abs/1711.11157, . [bibtex] [pdf]
[87], , and . Coded Machine Learning: Joint Informed Replication and Learning for Linear Regression, In Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing, . [bibtex] [pdf]
[86] and . Query Processing on Probabilistic Data: A Survey, Foundations and Trends in Databases, Now Publishers, . [bibtex] [pdf] [doi]
[85], and . Learning the Structure of Probabilistic Sentential Decision Diagrams, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), . [bibtex] [pdf]
Oral full presentation, acceptance rate 29/289 = 10%
[84], and . Probabilistic Program Abstractions, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), . [bibtex] [pdf]
[83], , , , and . Combining Stochastic Constraint Optimization and Probabilistic Programming: From Knowledge Compilation to Constraint Solving, In Proceedings of the 23rd International Conference on Principles and Practice of Constraint Programming (CP), . [bibtex] [pdf]
[82], and . Optimal Feature Selection for Decision Robustness in Bayesian Networks, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), . [bibtex] [pdf]
[81], and . Open-World Probabilistic Databases: An Abridged Report, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, . [bibtex] [pdf]
[80], , and . Don’t Fear the Bit Flips: Robust Linear Prediction Through Informed Channel Coding, In ICML 2017 Workshop on Reliable Machine Learning in the Wild, . [bibtex] [pdf]