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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I am an Assistant 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


[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]
[123], , and . Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns, In Proceedings of the 34th AAAI Conference on Artificial Intelligence, .
[122], and . Lecture Notes: Probabilistic Circuits: Representation and Inference, In , .
[121] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Ninth International Workshop on Statistical Relational AI (StarAI), . StarAI best paper award


[120], , , and . On Tractable Computation of Expected Predictions, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[119], , , and . Towards Hardware-Aware Tractable Learning of Probabilistic Models, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[118], , and . Smoothing Structured Decomposable Circuits, In Advances in Neural Information Processing Systems 32 (NeurIPS), . Oral spotlight presentation, acceptance rate 164/6743 = 2.4%
[117], , , 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), .

Recent Talks