## Talks

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

### Recent Invited Talks, Tutorials

### Recent Papers with Talks

## 2022 | |

[176] | Lossless Compression with Probabilistic Circuits, In Proceedings of the International Conference on Learning Representations (ICLR), 2022. . Oral spotlight presentation, acceptance rate 176/3391 = 5.2% |

[175] | Solving Marginal MAP Exactly by Probabilistic Circuit Transformations, In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. . |

## 2021 | |

[174] | Tractable Regularization of Probabilistic Circuits, In Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. . Oral spotlight presentation, acceptance rate 340/9122 = 3.7% |

[173] | A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference, In Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. . Oral full presentation, acceptance rate 55/9122 = 0.6% |

[172] | Probabilistic Sufficient Explanations, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021. . |

[171] | Probabilistic Generating Circuits, In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021. . Long presentation, acceptance rate 166/5513 = 3% |

[170] | On the Tractability of SHAP Explanations, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. . AAAI distinguished paper award |

[169] | Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. . |

[168] | Logical Abstractions for Noisy Variational Quantum Algorithm Simulation, In Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2021. . IEEE Micro top picks 2022 honorable mention |

## 2020 | |

[167] | On Effective Parallelization of Monte Carlo Tree Search, In Deep Reinforcement Learning Workshop at NeurIPS (DRLW), 2020. . |

[166] | Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations, In Advances in Neural Information Processing Systems 33 (NeurIPS), 2020. . Oral spotlight presentation, acceptance rate 385/9454 = 4.1% |

[165] | Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), 2020. . |

[164] | SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning, In Conference on Robot Learning, 2020. . |

[163] | Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, 2020. . ACM SIGPLAN distinguished paper award |

[162] | Relax, compensate and then integrate, In Proceedings of the ECML-PKDD Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), 2020. . |

### Older Invited Talks, Tutorials, etc.

##### Invited Talk — Nov 2021

Tractable Computation of Expected Kernels by Circuit Representations

Microsoft Research, New England

##### Tutorial — May 2020

Probabilistic Circuits: Inference, Representations, Learning and Theory

UCLA Computer Science Department - CS201 Seminar

##### Invited Talk — Jan 2020

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

Towards a New Synthesis of Reasoning and Learning

Northeastern University, Khoury College of Computer Sciences

##### Invited Talk — Feb 2019

Probabilistic and Logistic Circuits: A New Synthesis of Logic and Machine Learning

RelationalAI ArrowheadCon

##### Invited Talk — May 2018

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

PSDDs for Tractable Learning in Structured and Unstructured Spaces

Computer Science Department, University of British Columbia

##### Invited Talk — 2016

Probabilistic Reasoning by First-Order Model Counting

Workshop on Uncertainty in Computation, Simons Institute, Berkeley

##### Invited Talk — 2015

First-Order Knowledge Compilation for Probabilistic Reasoning

Symposium on New Frontiers in Knowledge Compilation, Vienna Center for Logic and Algorithms, Austria

##### Invited Tutorial — 2015

An Overview of Statistical Relational Learning

Alberto Mendelzon Graduate School on Data Management, Lima, Peru

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, Cornell University

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

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

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, University of Southern California

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, University of California, Irvine

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Cheriton School of Computer Science, University of Waterloo

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Computer Sciences Department, University of Wisconsin-Madison

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, Tufts University

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science and Informatics, Indiana University, Bloomington

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

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

##### Invited Talk — 2015

Scalable Inference and Learning for High-Level Probabilistic Models

Computer Science Department, University of California, Los Angeles

##### Invited Tutorial — 2014

Lifted inference in statistical relational models

International workshop on Big Uncertain Data (BUDA), ACM SIGMOD/PODS conference, Snowbird

##### 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

Recent advances in lifted inference at Leuven

Spring Workshop on Mining and Learning, Bad Neuenahr, Germany

##### Invited Talk — 2011

Monte-Carlo tree search for multi-player, no-limit Texas holdâ€™em poker

SIKS Symposium on Strategic Decision-Making in Complex Games, Maastricht University, Netherlands