Guy Van den Broeck

UCLA - Computer Science Department
Engineering VI Room 368A
404 Westwood Plaza
Los Angeles, CA 90095-1596
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guyvdb@cs.ucla.edu
guy@sigmoid.social
@guyvdb

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 (Tractable Deep Generative Models, Statistical Relational Learning, Probabilistic Programming), Knowledge Representation and Reasoning (Probabilistic Inference, Probabilistic Databases), and Artificial Intelligence in general.

Recent Publications

2023

[196], and . Image Inpainting via Tractable Steering of Diffusion Models, In Arxiv, .
[195] and . Collapsed Inference for Bayesian Deep Learning, In Advances in Neural Information Processing Systems 36 (NeurIPS), .
[194], and . A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints, In Advances in Neural Information Processing Systems 36 (NeurIPS), .
[193], , and . A Unified Approach to Count-Based Weakly Supervised Learning, In Advances in Neural Information Processing Systems 36 (NeurIPS), .
[192], , , and . On the Paradox of Learning to Reason from Data, In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), .
[191], , , , , and . Probabilistic Task-Adaptive Graph Rewiring, In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, .
[190], , , , and . Scaling Integer Arithmetic in Probabilistic Programs, In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), .
[189], , and . Tractable Control for Autoregressive Language Generation, In Proceedings of the 40th International Conference on Machine Learning (ICML), . Oral full presentation, acceptance rate 155/6538 = 2.4%
[188], , and . Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits, In Proceedings of the 40th International Conference on Machine Learning (ICML), .
[187], , and . SIMPLE: A Gradient Estimator for k-subset sampling, In Proceedings of the International Conference on Learning Representations (ICLR), .

Recent Talks