Workshop on Verification of Probabilistic Programs (VeriProP)

From Probabilistic Circuits to Probabilistic Programs and Back

International Conference on Agents and Artificial Intelligence (ICAART)

Probabilistic Circuits: Representations, Inference, Learning and Theory

The International Joint Conference on Artificial Intelligence and Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI)

From Probabilistic Circuits to Probabilistic Programs and Back ( video)

International Conference on Probabilistic Programming (PROBPROG)

From Probabilistic Circuits to Probabilistic Programs and Back

RWTH Aachen

Strudel: Learning Structured-Decomposable Probabilistic Circuits ( video)

International Conference on Probabilistic Graphical Models (PGM)

Computational Abstractions of Probability Distributions

International Conference on Probabilistic Graphical Models (PGM)

Probabilistic Circuits: Representations, Inference, Learning and Theory ( video)

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

Probabilistic Circuits: Representations, Inference, Learning and Theory

The 24th European Conference on Artificial Intelligence (ECAI)

Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing ( video)

ICML 2020

Probabilistic Circuits: Inference, Representations, Learning and Theory

UCLA Computer Science Department - CS201 Seminar

Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams ( video)

Symposium on Intelligent Data Analysis 2020

Probabilistic Circuits: Representations, Inference, Learning and Applications

The 34th AAAI Conference on Artificial Intelligence

Querying advanced probabilistic models: from relational embeddings to probabilistic programs

Ninth International Workshop on Statistical Relational AI (StarAI)

Towards a New Synthesis of Reasoning and Learning

CSE Colloquia Series, Washington University in St. Louis

Circuit Languages at the Confluence of Learning and Reasoning ( video)

Knowledge Representation & Reasoning Meets Machine Learning Workshop (KR2ML)

Smoothing Structured Decomposable Circuits ( video)

NeurIPS 2019

Tractable Probabilistic Models

Stanford Course on Deep Generative Models (CS236)

Reasoning about Missing Data in Machine Learning

Emerging Challenges in Databases and AI Research (DBAI)

Tractable Probabilistic Models

International Spring School on Uncertainty in AI and Data Management

Colloquium Talk at Harvey Mudd College

Tractable Probabilistic Models

The 35th International Conference on Logic Programming (ICLP-19)

Discrete Probabilistic Programming from First Principles

The 6th Workshop on Probabilistic Logic Programming (PLP)

At the Confluence of Logic and Learning

Dagstuhl Seminar Logic and Learning

Computers and Thought Award Lecture ( video)

The 28th International Joint Conference on Artificial Intelligence (IJCAI)

Discrete Probabilistic Programming from First Principles

IJCAI-2019 Workshop on Declarative Learning Based Programming (DeLBP-2019)

Generating and Sampling Orbits for Lifted Probabilistic Inference ( video)

Conference on Uncertainty in Artificial Intelligence (UAI-19)

Tractable Probabilistic Models

Conference on Uncertainty in Artificial Intelligence (UAI-19)

Active Inductive Logic Programming for Code Search

41st ACM/IEEE International Conference on Software Engineering (ICSE)

Towards Querying Probabilistic Knowledge Bases

First Conference on Automated Knowledge Base Construction (AKBC)

Circuit Languages as a Synthesis of Learning and Reasoning

Simons Symposium on New Directions in Theoretical Machine Learning

Towards a New Synthesis of Reasoning and Learning

Northeastern University, Khoury College of Computer Sciences

Towards a New Synthesis of Reasoning and Learning

University of Pennsylvania

Towards a New Synthesis of Reasoning and Learning

University of Toronto

Towards a New Synthesis of Reasoning and Learning

UC Berkeley EECS

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

RelationalAI ArrowheadCon

Learning Logistic Circuits

AAAI Conference on Artificial Intelligence

Factorized Exact Inference for Discrete Probabilistic Programs

LAFI 2019

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

Symposium on deep learning for complex relational data, KULeuven, Belgium

Approximate Knowledge Compilation by Online Collapsed Importance Sampling ( video)

NeurIPS 2018, Montreal, Canada

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

Stanford CS

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

Workshop on Hybrid Reasoning and Learning (HRL 2018) and Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances at KR 2018

On Robust Trimming of Bayesian Network Classifiers

IJCAI 2018

Sound Abstraction and Decomposition of Probabilistic Programs

International Conference on Machine Learning (ICML) 2018

A Semantic Loss Function for Deep Learning with Symbolic Knowledge ( video)

International Conference on Machine Learning (ICML) 2018

Probabilistic Circuits: A New Synthesis of Logic and Machine Learning

Computer Science Department, University of California, San Diego

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

Open-World Probabilistic Databases

3rd Global Conference on Artificial Intelligence (GCAI 2017), Florida

First-Order Knowledge Compilation

Dagstuhl Seminar on Recent Trends in Knowledge Compilation, Germany

PSDDs for Tractable Learning in Structured and Unstructured Spaces

Second International Workshop on Declarative Learning Based Programming (DeLBP), Melbourne, Australia

PSDDs for Tractable Learning in Structured and Unstructured Spaces

Computer Science Department, University of British Columbia

Open-World Probabilistic Databases

The 30th International FLAIRS Conference, Florida, USA

Tractable Learning in Structured Probability Spaces

The AAAI-17 Workshop on Symbolic Inference and Optimization (SymInfOpt-17), San Francisco

Tractable Learning in Structured Probability Spaces

Statistics Department Seminar, UCLA

Tractable Learning in Structured Probability Spaces

DTAI Seminar, KU Leuven, Belgium

Tractable Learning in Structured Probability Spaces

Southern California Machine Learning Symposium, Caltech

Probabilistic Reasoning by First-Order Model Counting ( video)

Workshop on Uncertainty in Computation, Simons Institute, Berkeley

Open-World Probabilistic Databases

International Conference on Scalable Uncertainty Management (SUM), Nice, France

First-Order Probabilistic Reasoning: Successes and Challenges

International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight, New York

Lifted Probabilistic Inference in Relational Models

International Joint Conference on Artificial Intelligence (IJCAI), New York

First-Order Knowledge Compilation

AAAI-16 Workshop on Beyond NP, Phoenix

Open-World Probabilistic Databases

Spring Workshop on Mining and Learning (SML), Titisee, Germany

Sentential Decision Diagrams And Their Applications

INFORMS Annual Meeting, Philadelphia

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam

First-Order Knowledge Compilation for Probabilistic Reasoning

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

Symmetry in Probabilistic Databases

Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru

An Overview of Statistical Relational Learning

Alberto Mendelzon Graduate School on Data Management, Lima, Peru

Approximate Symmetries in Lifted Inference ( video)

Banff Workshop on New Perspectives for Relational Learning, Banff, Canada

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, Cornell University

Scalable Inference and Learning for High-Level Probabilistic Models

Microsoft Research, Redmond

Scalable Inference and Learning for High-Level Probabilistic Models ( video)

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

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, University of Southern California

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, University of California, Irvine

Scalable Inference and Learning for High-Level Probabilistic Models

Cheriton School of Computer Science, University of Waterloo

Scalable Inference and Learning for High-Level Probabilistic Models

Computer Sciences Department, University of Wisconsin-Madison

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science, Tufts University

Scalable Inference and Learning for High-Level Probabilistic Models

Department of Computer Science and Informatics, Indiana University, Bloomington

Scalable Inference and Learning for High-Level Probabilistic Models

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

Scalable Inference and Learning for High-Level Probabilistic Models

Computer Science Department, University of California, Los Angeles

On the Role of Canonicity in Knowledge Compilation

AAAI Conference on Artificial Intelligence, Austin, Texas,

Lifted Probabilistic Inference for Asymmetric Graphical Models

AAAI Conference on Artificial Intelligence, Austin, Texas,

Lifted probabilistic inference in relational models

Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Canada

Lifted inference in statistical relational models

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

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

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

Lifted Inference and Learning in Statistical Relational Models,

Center for Data Science, University of Washington, Tacoma

On the Complexity and Approximation of Binary Evidence in Lifted Inference ( video)

Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe

Recent advances in lifted inference at Leuven

Spring Workshop on Mining and Learning, Bad Neuenahr, Germany

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference ( video)

25th Annual Conference on Neural Information Processing Systems (NIPS), Granada, Spain

Probabilistic programming in Scala

BeScala Meet-up, Belgium

Monte-Carlo tree search for multi-player, no-limit Texas hold’em poker

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

Lifted probabilistic inference by first-order knowledge compilation

IJCAI Tutorial on Lifted Inference in Probabilistic Logical Models, Barcelona, Spain

DTProbLog: A Decision-Theoretic Probabilistic Prolog

Twenty-fourth AAAI Conference on Artificial Intelligence, Atlanta