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.

Collaborators

Pedro Domingos

Robert Gens
Chloé Kiddon
Aniruddh Nath
Hoifung Poon

Jesse Davis
Vibhav Gogate
Stanley Kok
Daniel Lowd
Parag Singla

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 Chloé Kiddon and Pedro Domingos AAAI Conference on Artificial Intelligence, 2011. Full Paper (PDF)

Approximation by Quantization Vibhav Gogate and Pedro Domingos Uncertainty in Artificial Intelligence, 2011. Full Paper (PDF)

Probabilistic Theorem Proving Vibhav Gogate and Pedro Domingos Uncertainty in Artificial Intelligence, 2011. Full Paper (PDF)

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 Daniel Lowd and Pedro Domingos Annual Conference on Neural Information Processing Systems, 2010. Full Paper (PDF)

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 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 Aniruddh Nath and Pedro Domingos AAAI Conference on Artificial Intelligence, 2010. Full Paper (PDF)

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 Hoifung Poon and Pedro Domingos Annual Meeting of the Association for Computational Linguistics, 2010. Full Paper (PDF)

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 Vibhav Gogate and Pedro Domingos Workshop on Statistical Relational AI, 2010. Workshop Paper (PDF)

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 Vibhav Gogate and Pedro Domingos Uncertainty in Artificial Intelligence, 2010. Full Paper (PDF)

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 Stanley Kok and Pedro Domingos International Conference on Machine Learning, 2010. Full Paper (PDF)

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 Aniruddh Nath and Pedro Domingos International Workshop on Statistical Relational Learning, 2009. Workshop Paper (PDF)

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 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 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