Statistical Relational Learning

Intelligent agents must function in a world that is characterized by high uncertainty and missing information, and by a rich structure of objects, classes, and relations. Current AI systems are, for the most part, able to handle one of these issues but not both. Overcoming this will lay the foundation for the next generation of AI, bringing it significantly closer to human-level performance on the hardest problems. In particular, learning algorithms almost invariably assume that all training examples are mutually independent, but they often have complex relations among them. We are developing learners for this case, and applying them to domains like link-based Web search, adaptive Web navigation, viral marketing, and social network modeling. We are also developing statistical learning and inference techniques for time-changing relational domains, and applying them to fault diagnosis and other problems. More generally, our goal is to develop learners that can learn from noisy input in rich first-order languages, not just human-designed attribute vectors, and are thus much more autonomous and widely applicable.

Publications

Abductive Markov Logic for Plan Recognition
Parag Singla and Raymond J. Mooney
AAAI Conference on Artificial Intelligence, 2011. Full Paper (PDF)
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Chloé Kiddon and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2011. Full Paper (PDF)
Approximation by Quantization Approximation by Quantization
Vibhav Gogate and Pedro Domingos
Uncertainty in Artificial Intelligence, 2011. Full Paper (PDF)
Probabilistic Theorem Proving Probabilistic Theorem Proving
Vibhav Gogate and Pedro Domingos
Uncertainty in Artificial Intelligence, 2011. Full Paper (PDF)
Sum-Product Networks: A New Deep Architecture Sum-Product Networks: A New Deep Architecture
Hoifung Poon and Pedro Domingos
Uncertainty in Artificial Intelligence, 2011. Full Paper (PDF)
    Best Paper Award
Constraint Propagation for Efficient Inference in Markov Logic
Tivadar Papai, Parag Singla and Henry Kautz
International Conference on Principles and Practice of Constraint Programming, 2011. Full Paper (PDF)
Approximate Inference by Compilation to Arithmetic Circuits Approximate Inference by Compilation to Arithmetic Circuits
Daniel Lowd and Pedro Domingos
Annual Conference on Neural Information Processing Systems, 2010. Full Paper (PDF)
Learning Efficient Markov Networks Learning Efficient Markov Networks
Vibhav Gogate, William Austin Webb and Pedro Domingos
Annual Conference on Neural Information Processing Systems, 2010. Full Paper (PDF)
Lifted Inference Seen from the Other Side: The Tractable Features Lifted Inference Seen from the Other Side: The Tractable Features
Abhay K Jha, Vibhav Gogate, Alexandra Meliou and Dan Suciu
Annual Conference on Neural Information Processing Systems, 2010. Full Paper (PDF)
Efficient Belief Propagation for Utility Maximization and Repeated Inference Efficient Belief Propagation for Utility Maximization and Repeated Inference
Aniruddh Nath and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2010. Full Paper (PDF)
Efficient Lifting for Online Probabilistic Inference Efficient Lifting for Online Probabilistic Inference
Aniruddh Nath and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2010. Full Paper (PDF) (Dataset)
Unsupervised Ontology Induction from Text Unsupervised Ontology Induction from Text
Hoifung Poon and Pedro Domingos
Annual Meeting of the Association for Computational Linguistics, 2010. Full Paper (PDF)
Approximate Lifted Belief Propagation Approximate Lifted Belief Propagation
Parag Singla, Aniruddh Nath and Pedro Domingos
Workshop on Statistical Relational AI, 2010. Workshop Paper (PDF)
Exploiting Logical Structure in Lifted Probabilistic Inference Exploiting Logical Structure in Lifted Probabilistic Inference
Vibhav Gogate and Pedro Domingos
Workshop on Statistical Relational AI, 2010. Workshop Paper (PDF)
Leveraging Ontologies for Lifted Probabilistic Inference and Learning Leveraging Ontologies for Lifted Probabilistic Inference and Learning
Chloé Kiddon and Pedro Domingos
Workshop on Statistical Relational AI, 2010. Workshop Paper
Formula-Based Probabilistic Inference Formula-Based Probabilistic Inference
Vibhav Gogate and Pedro Domingos
Uncertainty in Artificial Intelligence, 2010. Full Paper (PDF)
Bottom-Up Learning of Markov Network Structure Bottom-Up Learning of Markov Network Structure
Jesse Davis and Pedro Domingos
International Conference on Machine Learning, 2010. Full Paper (PDF)
Learning Markov Logic Networks Using Structural Motifs Learning Markov Logic Networks Using Structural Motifs
Stanley Kok and Pedro Domingos
International Conference on Machine Learning, 2010. Full Paper (PDF)
Unsupervised Semantic Parsing Unsupervised Semantic Parsing
Hoifung Poon and Pedro Domingos
Conference on Empirical Methods in Natural Language Processing, 2009. Full Paper (PDF)
    Best Paper Award
A Language for Relational Decision Theory A Language for Relational Decision Theory
Aniruddh Nath and Pedro Domingos
International Workshop on Statistical Relational Learning, 2009. Workshop Paper (PDF)
Deep Transfer via Second-Order Markov Logic Deep Transfer via Second-Order Markov Logic
Jesse Davis and Pedro Domingos
International Conference on Machine Learning, 2009. Full Paper (PDF)
Learning Markov Logic Network Structure via Hypergraph Lifting Learning Markov Logic Network Structure via Hypergraph Lifting
Stanley Kok and Pedro Domingos
International Conference on Machine Learning, 2009. Full Paper (PDF)
Joint Unsupervised Coreference Resolution with Markov Logic
Hoifung Poon and Pedro Domingos
Conference on Empirical Methods in Natural Language Processing, 2008. Full Paper (PDF)
A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC
Hoifung Poon, Pedro Domingos and Marc Sumner
AAAI Conference on Artificial Intelligence, 2008. Full Paper (PDF) (Slides) (Code)
Hybrid Markov Logic Networks
Jue Wang and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2008. Full Paper (PDF) (Slides) (Code)
Lifted First-Order Belief Propagation
Parag Singla and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2008. Full Paper (PDF)
Learning arithmetic circuits
Daniel Lowd and Pedro Domingos
Uncertainty in Artificial Intelligence, 2008. Full Paper (PDF)
Extracting Semantic Networks from Text via Relational Clustering
Stanley Kok and Pedro Domingos
European Conference on Machine Learning, 2008. Full Paper (PDF)
Joint Inference in Information Extraction
Hoifung Poon and Pedro Domingos
AAAI Spring Symposium Series, 2007. Full Paper (PDF)
Recursive Random Fields
Daniel Lowd and Pedro Domingos
International Joint Conference on Artificial Intelligence, 2007. Full Paper (PDF)
Statistical Predicate Invention
Stanley Kok and Pedro Domingos
International Conference on Machine Learning, 2007. Full Paper (PDF)
Markov Logic in Infinite Domains
Parag Singla and Pedro Domingos
Uncertainty in Artificial Intelligence, 2007. Full Paper (PDF)
Structured Machine Learning: Ten Problems for the Next Ten Years
Pedro Domingos
International Conference on Inductive Logic Programming, 2007. Full Paper (PDF)
Efficient Weight Learning for Markov Logic Networks
Daniel Lowd and Pedro Domingos
European Conference on Principles and Practice of Knowledge Discovery in Databases, 2007. Full Paper (PDF)
Memory-Efficient Inference in Relational Domains
Parag Singla and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2006. Full Paper (PDF)
Sound and Efficient Inference with Probabilistic and Deterministic Dependencies
Hoifung Poon and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2006. Full Paper (PDF)
Unifying Logical and Statistical AI
Pedro Domingos, Stanley Kok, Hoifung Poon, Matt Richardson and Parag Singla
AAAI Conference on Artificial Intelligence, 2006. Full Paper
Entity Resolution with Markov Logic
Parag Singla and Pedro Domingos
IEEE International Conference on Data Mining, 2006. Full Paper (PDF)
Markov Logic Networks
Matt Richardson and Pedro Domingos
Machine Learning Journal, 2006. Journal Article (PDF)
Discriminative Training of Markov Logic Networks
Parag Singla and Pedro Domingos
AAAI Conference on Artificial Intelligence, 2005. Full Paper (PDF)
Learning the Structure of Markov Logic Networks
Stanley Kok and Pedro Domingos
International Conference on Machine Learning, 2005. Full Paper (PDF)
Object identification with attribute-mediated dependencies
Parag Singla and Pedro Domingos
European Conference on Principles and Practice of Knowledge Discovery in Databases, 2005. Full Paper (PDF)
    Best Paper Award
Mining social networks for viral marketing
Pedro Domingos
IEEE Intelligent Systems, 2005. Journal Article (PDF)
Multi-relational record linkage
Parag Singla and Pedro Domingos
KDD Workshop on Multi-Relational Data Mining, 2004. Workshop Paper (PDF)
Trust management for the Semantic Web
Matt Richardson, Rakesh Agrawal and Pedro Domingos
International Semantic Web Conference, 2003. Full Paper (PDF)
Learning with knowledge from multiple experts
Matt Richardson and Pedro Domingos
International Conference on Machine Learning, 2003. Full Paper (PDF)
Building large knowledge bases by mass collaboration
Matt Richardson and Pedro Domingos
International Conference on Knowledge Capture, 2003. Full Paper (PDF)
Mining massive relational databases
Geoff Hulten, Pedro Domingos and Yeuhi Abe
International Workshop on Statistical Relational Learning, 2003. Workshop Paper (PDF)
Research on statistical relational learning at the University of Washington
Pedro Domingos, Yeuhi Abe, Corin Anderson, AnHai Doan, Dieter Fox, Alon Halevy, Geoff Hulten, Henry Kautz, Tessa Lau, Lin Liao, Madhavan Madhavan, Mausam, Donald J Patterson, Matt Richardson, Sumit Sanghai, Daniel S. Weld and Steve Wolfman
International Workshop on Statistical Relational Learning, 2003. Workshop Paper (PDF)
Mining knowledge-sharing sites for viral marketing
Matt Richardson and Pedro Domingos
Knowledge Discovery and Data Mining, 2002. Full Paper (PDF)
Relational Markov models and their application to adaptive Web navigation
Corin Anderson, Pedro Domingos and Daniel S. Weld
Knowledge Discovery and Data Mining, 2002. Full Paper (PDF)
The intelligent surfer: Probabilistic combination of link and content information in PageRank
Matt Richardson and Pedro Domingos
Annual Conference on Neural Information Processing Systems, 2001. Full Paper (PDF)
Mining the network value of customers
Pedro Domingos and Matt Richardson
Knowledge Discovery and Data Mining, 2001. Full Paper (PDF)
Mining high-speed data streams
Geoff Hulten and Pedro Domingos
Knowledge Discovery and Data Mining, 2000. Full Paper (PDF)

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Slides:
  Practical Statistical Relational AI