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.

Talks and Tutorials

Past

2017
Keynote: Open-World Probabilistic Databases [pdf], 3rd Global Conference on Artificial Intelligence (GCAI 2017), Florida, USA
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
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

[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]
[79], and . Probabilistic Program Abstractions, In Seventh International Workshop on Statistical Relational AI (StarAI), . [bibtex] [pdf]
[78], , and . Domain Recursion for Lifted Inference with Existential Quantifiers, In Seventh International Workshop on Statistical Relational AI (StarAI), . [bibtex] [pdf]