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Statistical Relational Learning
Overview
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.
Software
Alchemy
Publications
- M. Richardson and P. Domingos,
Markov Logic Networks. Machine Learning. To appear.
- P. Domingos,
Mining Social Networks for Viral Marketing. IEEE Intelligent
Systems, 20(1), 80-82, 2005.
- S. Kok and P. Domingos,
Learning the Structure of Markov Logic Networks.
Proceedings of the Twenty-Second International Conference on Machine
Learning (pp. 441-448), 2005. Bonn, Germany: ACM Press.
- P. Singla and P. Domingos,
Discriminative Training of Markov Logic Networks.
Proceedings of the Twentieth National Conference on Artificial Intelligence
(pp. 868-873), 2005. Pittsburgh, PA: AAAI Press.
- P. Singla and P. Domingos,
Object Identification with Attribute-Mediated Dependences.
Proceedings of the Ninth European Conference on Principles and Practice of
Knowledge Discovery in Databases, 2005. Porto, Portugal: Springer.
- P. Domingos and M. Richardson,
Markov Logic: A Unifying Framework for Statistical Relational Learning.
Proceedings of the ICML-2004 Workshop on Statistical Relational
Learning and its Connections to Other Fields (pp. 49-54), 2004.
Banff, Canada: IMLS.
- P. Domingos, Y. Abe, C. Anderson, A. Doan, D. Fox, A. Halevy,
G. Hulten, H. Kautz, T. Lau, L. Liao, J. Madhavan, Mausam,
D. Patterson, M. Richardson, S. Sanghai, D. Weld and S. Wolfman,
Research on Statistical Relational Learning at the University of
Washington. Proceedings of the IJCAI-2003 Workshop on Learning
Statistical Models from Relational Data, 2003. Acapulco, Mexico: IJCAII.
- G. Hulten, P. Domingos and Y. Abe,
Mining Massive Relational Databases. Proceedings of the
IJCAI-2003 Workshop on Learning Statistical Models from Relational
Data, 2003. Acapulco, Mexico: IJCAII.
- C. Anderson, P. Domingos and D. Weld,
Relational Markov Models and their Application to Adaptive Web
Navigation. Proceedings of the
Eighth International Conference on Knowledge Discovery and Data Mining
(pp. 143-152), 2002. Edmonton, Canada: ACM Press.
- M. Richardson and P. Domingos,
Mining Knowledge-Sharing Sites for Viral Marketing. Proceedings of
the Eighth International Conference on Knowledge Discovery and Data
Mining (pp. 61-70), 2002. Edmonton, Canada: ACM Press.
- M. Richardson and P. Domingos,
The Intelligent Surfer: Probabilistic Combination of Link and Content
Information in PageRank. Advances in Neural Information Processing
Systems 14 (pp. 1441-1448), 2002. Cambridge, MA: MIT Press.
- P. Domingos and M. Richardson,
Mining the Network Value of Customers. Proceedings of the Seventh
International Conference on Knowledge Discovery and Data Mining
(pp. 57-66), 2001. San Francisco, CA: ACM Press.
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