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. I previously was a postdoctoral researcher at UCLA’s Automated Reasoning lab and KU Leuven’s Declarative Languages and Artificial Intelligence lab.

Talks and Tutorials

Future

2017
Keynote: 3rd Global Conference on Artificial Intelligence (GCAI 2017), Florida, USA

Past

2017
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
2016
Invited Talk: Tractable Learning in Structured Probability Spaces [pdf], DTAI Seminar, KU Leuven, Belgium
Invited Talk: Tractable Learning in Structured Probability Spaces [pdf], Southern California Machine Learning Symposium, Caltech
Invited Talk: Probabilistic Reasoning by First-Order Model Counting [pdf] [video], Workshop on Uncertainty in Computation, Simons Institute, Berkeley
Invited Talk: Open-World Probabilistic Databases [pdf], International Conference on Scalable Uncertainty Management (SUM), Nice, France
Invited Talk: First-Order Probabilistic Reasoning: Successes and Challenges [pdf], International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight
Tutorial: Lifted Probabilistic Inference in Relational Models [pdf], International Joint Conference on Artificial Intelligence (IJCAI), Co-authored with Dan Suciu.
Invited Talk: First-Order Knowledge Compilation [pdf], AAAI-16 Workshop on Beyond NP, Phoenix
Talk: Open-World Probabilistic Databases [pdf], Spring Workshop on Mining and Learning (SML), Titisee, Germany

Recent Publications

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

2017

[76], and . Optimal Feature Selection for Decision Robustness in Bayesian Networks, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), . [bibtex] [pdf]
[75], 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]

2016

[74], , and . New Liftable Classes for First-Order Probabilistic Inference, In Advances in Neural Information Processing Systems 29 (NIPS), . [bibtex] [pdf]
[73], and . Algebraic Model Counting, In International Journal of Applied Logic, . [bibtex] [pdf] [doi]
[72], , and . Robust Channel Coding Strategies for Machine Learning Data, In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, . [bibtex] [pdf]
[71], and . Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, . [bibtex] [pdf]
[70]. First-Order Model Counting in a Nutshell, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight Track, . [bibtex] [pdf]
[69], , , and . Tp-Compilation for Inference in Probabilistic Logic Programs, In International Journal of Approximate Reasoning, . [bibtex] [pdf] [doi]
[68], and . Open-World Probabilistic Databases, In Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning (KR), . [bibtex] [pdf]
KR best student paper award
[67], , and . Exploiting Local and Repeated Structure in Dynamic Bayesian Networks, In Artificial Intelligence, volume 232, . [bibtex] [pdf] [doi]