Guy Van den Broeck

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596
+1 (310) 206-6552
Pronounce my name
guyvdb@cs.ucla.edu

I am an Associate 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.

Recent Publications

2021

[151], and . Probabilistic Sufficient Explanations, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), .
[150], , , , and . Model Checking Finite-Horizon Markov Chains with Probabilistic Inference, In Proceedings of the 33rd International Conference on Computer-Aided Verification (CAV), .
[149], and . Probabilistic Generating Circuits, In Proceedings of the 38th International Conference on Machine Learning (ICML), . Long presentation, acceptance rate 166/5513 = 3%
[148], and . Open-World Probabilistic Databases: Semantics, Algorithms, Complexity, In Artificial Intelligence, .  [doi]
[147], , and . On the Tractability of SHAP Explanations, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .   AAAI distinguished paper award
[146], and . Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .
[145], , , and . Juice: A Julia Package for Logic and Probabilistic Circuits, In Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track), .
[144], , , and . Logical Abstractions for Noisy Variational Quantum Algorithm Simulation, In Architectural Support for Programming Languages and Operating Systems (ASPLOS), .

2020

[143], , , 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%
[142], , and . Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), .

Recent Talks

Invited Talk — Apr 2021  
Invited Talk — Apr 2021