Statistical and Relational Artificial Intelligence Lab

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596

The Statistical and Relational Artificial Intelligence (StarAI) lab is directed by Prof. Guy Van den Broeck. The StarAI lab performs research on 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

2019

[103], , and . Active Inductive Logic Programming for Code Search, In The 41st ACM/IEEE International Conference on Software Engineering (ICSE), .
[102], , , and . Scalable Rule Learning in Probabilistic Knowledge Bases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[101] and . On Constrained Open-World Probabilistic Databases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[100], and . The Institutional Life of Algorithms: Lessons from California's Money Bail Reform Act, In The 8th Annual Conference On Robotics, Law & Policy, .
[99] and . Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), . Oral full presentation, acceptance rate 460/7700 = 6%

2018

[98], , , and . Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing, In Advances in Neural Information Processing Systems 31 (NeurIPS), .
[97] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Advances in Neural Information Processing Systems 31 (NeurIPS), . 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, .
[95], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), .
[94], and . Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), .

Recent Talks