AI can learn from data. But can it learn to reason?

ICML Workshop on Knowledge and Logical Reasoning in the Era of Data-driven Learning

AI can learn from data. But can it learn to reason?

UC Irvine AI/ML Seminar Series

AI can learn from data. But can it learn to reason?

ICLR Workshop on Neurosymbolic Generative Models (NeSy-GeMs)

AI can learn from data. But can it learn to reason?

Workshop on Machine Learning, Interpretability, and Logic

Tractable Deep Generative Models

Dagstuhl Seminar on Challenges and Perspectives in Deep Generative Modeling

AI can learn from data. But can it learn to reason?

Oregon State University AI Seminar

AI can learn from data. But can it learn to reason?

IBM Neuro-Symbolic AI Workshop

AI can learn from data. But can it learn to reason?

Neuro Causal and Symbolic AI Workshop at NeurIPS

AI can learn from data. But can it learn to reason?

The 18th Reasoning Web Summer School

AI can learn from data. But can it learn to reason?

TTI/Vanguard

AI can learn from data. But can it learn to reason?

Tong Lecture Series at Peking University

Tractable Probabilistic Circuits

ICML Workshop Beyond Bayes: Paths Towards Universal Reasoning Systems

Bridging Data and Knowledge in Neuro-Symbolic Learning

Summer School on Neurosymbolic Programming

Tractable Probabilistic Circuits

VinAI Research

Data and Knowledge in Neuro-Symbolic Learning

Siemens AI Talks

Tractable Probabilistic Circuits

Dagstuhl Seminar on Recent Advancements in Tractable Probabilistic Inference

Computational Probabilistic Models

Perspectives in AI seminar of the C4AI

Tractable Probabilistic Circuits

Simons Workshop on Algorithmic Aspects of Causal Inference

Knowledge and Data in Neuro-Symbolic Learning

Summit of the Mellichamp Mind and Machine Initiative

Recent Developments in Probabilistic Circuits

Google

Tractable Computation of Expected Kernels by Circuit Representations

Microsoft Research, New England

Reasoning About Learned Models' Behavior

Open Data Science Conference West (ODSC West)

Reasoning About Learned Models' Behavior

15th International Workshop On Neural-symbolic Learning And Reasoning

Reasoning About Learned Models' Behavior

Human-Centered AI Conference, Pepperdine

Recent Advances in Discrete Probabilistic Program Inference

Workshop on Verification of Probabilistic Programs (VeriProP)

Reasoning About the Probabilistic Behavior of Classifiers

International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR)

Tractable Computation of Expected Kernels by Circuits

Yahoo Research Seminar

Tractable Computation of Expected Kernels by Circuits

The Alan Turing Institute

From Probabilistic Circuits to Probabilistic Programs and Back

Los Alamos National Laboratory

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

International Conference on Probabilistic Programming (PROBPROG)

From Probabilistic Circuits to Probabilistic Programs and Back

RWTH Aachen

Computational Abstractions of Probability Distributions

International Conference on Probabilistic Graphical Models (PGM)

Probabilistic Circuits: Representations, Inference, Learning and Theory

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)

Probabilistic Circuits: Inference, Representations, Learning and Theory

UCLA Computer Science Department - CS201 Seminar

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

Knowledge Representation & Reasoning Meets Machine Learning Workshop (KR2ML)

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

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)

Tractable Probabilistic Models

Conference on Uncertainty in Artificial Intelligence (UAI-19)

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

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

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

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

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

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

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

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

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

Recent advances in lifted inference at Leuven

Spring Workshop on Mining and Learning, Bad Neuenahr, Germany

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