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


[127], and . Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration, In Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), .
[126], , , , and . Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search, In International Conference on Learning Representations, .
[125], , , and . Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams, In Proceedings of the Symposium on Intelligent Data Analysis (IDA), .
[124] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Ninth International Workshop on Statistical Relational AI (StarAI), .
[123], and . Lecture Notes: Probabilistic Circuits: Representation and Inference, In , .
[122], , and . Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns, In Proceedings of the 34th AAAI Conference on Artificial Intelligence, .


[121], , , and . On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications, In Proceedings of the NeurIPS Workshop on Energy Efficient Machine Learning and Cognitive Computing (EMC2), .
[120], , , and . On Tractable Computation of Expected Predictions, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[119], , , and . Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing, In Proceedings of the NeurIPS Workshop on Knowledge Representation and Reasoning Meets Machine Learning (KR2ML), .
[118], , , and . Towards Hardware-Aware Tractable Learning of Probabilistic Models, In Advances in Neural Information Processing Systems 32 (NeurIPS), .

Recent Talks