Guy Van den Broeck

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596
+1 (310) 206-6552
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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

2020

[133], and . On the Relationship Between Probabilistic Circuits and Determinantal Point Processes, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[132] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[131], and . Towards Probabilistic Sufficient Explanations, In Extending Explainable AI Beyond Deep Models and Classifiers Workshop at ICML (XXAI), .
[130], , , and . Handling Missing Data in Decision Trees: A Probabilistic Approach, In The Art of Learning with Missing Values Workshop at ICML (Artemiss), .
[129], , , and . Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing, In Proceedings of the 37th International Conference on Machine Learning (ICML), .
[128], , , , , , , and . Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits, In Proceedings of the 37th International Conference on Machine Learning (ICML), .
[127], , and . Counterexample-Guided Learning of Monotonic Neural Networks, .
[126], 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), .
[125], , , and . Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams, In Proceedings of the Symposium on Intelligent Data Analysis (IDA), .
[124], , , and . Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning, In Entropy, volume 22, .  [doi]

Recent Talks