Talks

Click icons to see presentation slides and videos for the talks and papers below.

Recent Papers with Talks

2022

[171], and . Lossless Compression with Probabilistic Circuits, In Proceedings of the International Conference on Learning Representations (ICLR), . Oral spotlight presentation, acceptance rate 176/3391 = 5.2%
[170], and . Solving Marginal MAP Exactly by Probabilistic Circuit Transformations, In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), .

2021

[169] and . Tractable Regularization of Probabilistic Circuits, In Advances in Neural Information Processing Systems 35 (NeurIPS), . Oral spotlight presentation, acceptance rate 340/9122 = 3.7%
[168], , , and . A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference, In Advances in Neural Information Processing Systems 35 (NeurIPS), . Oral full presentation, acceptance rate 55/9122 = 0.6%
[167], and . Probabilistic Sufficient Explanations, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), .
[166], and . Probabilistic Generating Circuits, In Proceedings of the 38th International Conference on Machine Learning (ICML), . Long presentation, acceptance rate 166/5513 = 3%
[165], , and . On the Tractability of SHAP Explanations, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .   AAAI distinguished paper award
[164], and . Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .
[163], , , and . Logical Abstractions for Noisy Variational Quantum Algorithm Simulation, In Architectural Support for Programming Languages and Operating Systems (ASPLOS), . IEEE Micro top picks 2022 honorable mention

2020

[162], , , and . On Effective Parallelization of Monte Carlo Tree Search, In Deep Reinforcement Learning Workshop at NeurIPS (DRLW), .  
[161], , , 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%
[160], , and . Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), .
[159], , , , and . SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning, In Conference on Robot Learning, .
[158], and . Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, .  [doi] ACM SIGPLAN distinguished paper award
[157], , , and . Relax, compensate and then integrate, In Proceedings of the ECML-PKDD Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), .  

Older Invited Talks, Tutorials, etc.

Invited Talk — Apr 2021  
Invited Talk — Apr 2021  
Tutorial — May 2020  
Slides

Probabilistic Circuits: Inference, Representations, Learning and Theory

UCLA Computer Science Department - CS201 Seminar

Invited Talk — Jan 2020  
Slides

Towards a New Synthesis of Reasoning and Learning

CSE Colloquia Series, Washington University in St. Louis

Invited Talk — Oct 2019

Colloquium Talk at Harvey Mudd College

Invited Talk — Apr 2019  
Slides

Towards a New Synthesis of Reasoning and Learning

Northeastern University, Khoury College of Computer Sciences

Invited Talk — Apr 2019  
Invited Talk — Mar 2019  
Invited Talk — Feb 2019  
Invited Talk — Feb 2019  
Invited Talk — May 2018  
Slides

Probabilistic Circuits: A New Synthesis of Logic and Machine Learning

Computer Science Department, University of California, San Diego

Panelist — 2018

Women & Philanthropy Spring Event on Artificial Intelligence, University of California, Los Angeles

Talk — 2017  
Slides

PSDDs for Tractable Learning in Structured and Unstructured Spaces

Computer Science Department, University of British Columbia

Invited Talk — 2017  
Slides

Tractable Learning in Structured Probability Spaces

Statistics Department Seminar, UCLA

Invited Talk — 2016  
Slides

Tractable Learning in Structured Probability Spaces

DTAI Seminar, KU Leuven, Belgium

Invited Talk — 2015  
Invited Tutorial — 2015

An Overview of Statistical Relational Learning

Alberto Mendelzon Graduate School on Data Management, Lima, Peru

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, Cornell University

Invited Talk — 2015  
Invited Talk — 2015  

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science & Engineering, University of Washington, Seattle

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, University of Southern California

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, University of California, Irvine

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Cheriton School of Computer Science, University of Waterloo

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Computer Sciences Department, University of Wisconsin-Madison

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, Tufts University

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science and Informatics, Indiana University, Bloomington

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne

Invited Talk — 2015  
Slides

Scalable Inference and Learning for High-Level Probabilistic Models

Computer Science Department, University of California, Los Angeles

Invited Tutorial — 2014  
Invited Talk — 2014

ECCAI Dissertation Award Ceremony at the European Conference on Artificial Intelligence (ECAI), , Prague, Czech Republic

Invited Talk — 2014

Scientific prize IBM Belgium for Informatics Award Ceremony, IBM, Brussels, Belgium

Invited Talk — 2014

Lifted Inference and Learning in Statistical Relational Models,

Center for Data Science, University of Washington, Tacoma

Talk — 2012  
Slides

Recent advances in lifted inference at Leuven

Spring Workshop on Mining and Learning, Bad Neuenahr, Germany

Talk — 2011  
Slides

Probabilistic programming in Scala

BeScala Meet-up, Belgium