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.

Talks and Tutorials

Future

2019
Invited Talk: First Conference on Automated Knowledge Base Construction.

Past

2018
Invited Talk: Probabilistic and Logistic Circuits: A New Synthesis of Logic and Machine Learning [pdf], Symposium on Deep learning for complex relational data, KULeuven, Belgium
Talk: Probabilistic and Logistic Circuits: A New Synthesis of Logic and Machine Learning [pdf], Stanford CS
Invited Talk: Probabilistic and Logistic Circuits: A New Synthesis of Logic and Machine Learning [pdf], Workshop on Hybrid Reasoning and Learning (HRL 2018) and Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances at KR 2018
Invited Talk: Probabilistic Circuits: A New Synthesis of Logic and Machine Learning [pdf], Computer Science Department, University of California, San Diego
Panelist: Women & Philanthropy Spring Event on Artificial Intelligence, University of California, Los Angeles
2017
Keynote: Open-World Probabilistic Databases [pdf], 3rd Global Conference on Artificial Intelligence (GCAI 2017), Florida
Invited Talk: First-Order Knowledge Compilation [pdf], Dagstuhl Seminar on Recent Trends in Knowledge Compilation
Invited Talk: PSDDs for Tractable Learning in Structured and Unstructured Spaces [pdf], Second International Workshop on Declarative Learning Based Programming (DeLBP), Melbourne, Australia
Talk: PSDDs for Tractable Learning in Structured and Unstructured Spaces [pdf], Computer Science Department, University of British Columbia
Invited Talk: Open-World Probabilistic Databases [pdf], The 30th International FLAIRS Conference, Florida, USA
Invited Talk: Tractable Learning in Structured Probability Spaces [pdf], The AAAI-17 Workshop on Symbolic Inference and Optimization (SymInfOpt-17), San Francisco
Invited Talk: Tractable Learning in Structured Probability Spaces [pdf], Statistics Department Seminar, UCLA

Recent Publications

For more details, also see the lists   By Year,  By Publication Type,  By Google Scholar,  RSS feed (subscribe),  BibTex

2019

[98] and . Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), . [bibtex] [pdf]

2018

[97] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Advances in Neural Information Processing Systems 31 (NIPS), . [bibtex] [pdf]
Oral full presentation, acceptance rate 30/4856 = 0.6%
[96] and . Learning Logistic Circuits, In Proceedings of the UAI 2018 Workshop: Uncertainty in Deep Learning, . [bibtex] [pdf]
[95] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), . [bibtex] [pdf]
[94], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), . [bibtex] [pdf]
[93] and . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), . [bibtex] [pdf]
[92], and . Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), . [bibtex] [pdf]
[91], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), . [bibtex] [pdf]
[90] and . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), . [bibtex] [pdf]
[89], and . Probabilistic Program Inference With Abstractions, In POPL 2018 Probabilistic Programming Languages, Semantics, and Systems Workshop, . [bibtex] [pdf]