# 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.

## People

## Publications

- Learning Relational Sum-Product Networks (2015)
- Approximate Lifting Techniques for Belief Propagation (2014)
- Automated Debugging with Tractable Probabilistic Programming (2014)
- Tractability through Exchangeability: A New Perspective on Efficient Lifted Inference (2014)
- Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond (2014)
- Tractable Probabilistic Knowledge Bases with Existence Uncertainty (2013)
- A Tractable First-Order Probabilistic Logic (2012)
- Learning Multiple Hierarchical Relational Clusterings (2012)
- Abductive Markov Logic for Plan Recognition (2011)
- Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models (2011)
- Approximation by Quantization (2011)
- Constraint Propagation for Eﬃcient Inference in Markov Logic (2011)
- Probabilistic Theorem Proving (2011)
- Sum-Product Networks: A New Deep Architecture (2011)
- Approximate Inference by Compilation to Arithmetic Circuits (2010)
- Learning Efficient Markov Networks (2010)
- Lifted Inference Seen from the Other Side: The Tractable Features (2010)
- Approximate Lifted Belief Propagation (2010)
- Efficient Belief Propagation for Utility Maximization and Repeated Inference (2010)
- Efficient Lifting for Online Probabilistic Inference (2010)
- Exploiting Logical Structure in Lifted Probabilistic Inference (2010)
- Formula-Based Probabilistic Inference (2010)
- Leveraging Ontologies for Lifted Probabilistic Inference and Learning (2010)
- Unsupervised Ontology Induction from Text (2010)
- Bottom-Up Learning of Markov Network Structure (2010)
- Learning Markov Logic Networks Using Structural Motifs (2010)
- Unsupervised Semantic Parsing (2009)
- A Language for Relational Decision Theory (2009)
- Deep Transfer via Second-Order Markov Logic (2009)
- Learning Markov Logic Network Structure via Hypergraph Lifting (2009)
- Joint Unsupervised Coreference Resolution with Markov Logic (2008)
- A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC (2008)
- Hybrid Markov Logic Networks (2008)
- Lifted First-Order Belief Propagation (2008)
- Extracting Semantic Networks from Text via Relational Clustering (2008)
- Learning Arithmetic Circuits (2008)
- Joint Inference in Information Extraction (2007)
- Recursive Random Fields (2007)
- Efficient Weight Learning for Markov Logic Networks (2007)
- Markov Logic in Infinite Domains (2007)
- Statistical Predicate Invention (2007)
- Structured Machine Learning: Ten Problems for the Next Ten Years (2007)
- Entity Resolution with Markov Logic (2006)
- Markov Logic Networks (2006)
- Memory-Efficient Inference in Relational Domains (2006)
- Sound and Efficient Inference with Probabilistic and Deterministic Dependencies (2006)
- Unifying Logical and Statistical AI (2006)
- Discriminative Training of Markov Logic Networks (2005)
- Learning the Structure of Markov Logic Networks (2005)
- Mining social networks for viral marketing (2005)
- Object identification with attribute-mediated dependencies (2005)
- Multi-relational record linkage (2004)
- Trust management for the Semantic Web (2003)
- Building large knowledge bases by mass collaboration (2003)
- Learning with knowledge from multiple experts (2003)
- Mining massive relational databases (2003)
- Research on Statistical Relational Learning at the University of Washington (2003)
- Mining knowledge-sharing sites for viral marketing (2002)
- Relational Markov models and their application to adaptive Web navigation (2002)
- Mining the network value of customers (2001)
- The intelligent surfer: Probabilistic combination of link and content information in PageRank (2001)
- Mining high-speed data streams (2000)