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

2021

[150], and . Open-World Probabilistic Databases: Semantics, Algorithms, Complexity, In Artificial Intelligence, .  [doi]
[149], , , and . Juice: A Julia Package for Logic and Probabilistic Circuits, In Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track), .
[148], , and . On the Tractability of SHAP Explanations, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, . AAAI distinguished paper award
[147], and . Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .
[146], , , and . Logical Abstractions for Noisy Variational Quantum Algorithm Simulation, In Architectural Support for Programming Languages and Operating Systems (ASPLOS), .

2020

[145], , , and . Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations, In Advances in Neural Information Processing Systems 33 (NeurIPS), . Oral spotlight presentation, acceptance rate 385/9454 = 4.1%
[144], , and . Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), .
[143], , , and . On Effective Parallelization of Monte Carlo Tree Search, In Deep Reinforcement Learning Workshop at NeurIPS (DRLW), .
[142], , , , and . SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning, In Conference on Robot Learning, .
[141], and . Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, .  [doi] ACM SIGPLAN distinguished paper award

Recent Talks

TutorialMay 2020
Slides

Probabilistic Circuits: Inference, Representations, Learning and Theory

UCLA Computer Science Department - CS201 Seminar