CS590N Statistical Relational Learning
Spring 2007: Schedule of Readings

• Background: Getoor, L. and C. Diehl (2005). Link Mining: A Survey. SIGKDD Explorations, December, 2005, Volume 7 Issue 2.

• McGovern, A., L. Friedland, M. Hay, B. Gallagher, A. Fast, J. Neville, and D. Jensen (2003). Exploiting Relational Structure to Understand Publication Patterns in High-Energy Physics. SIGKDD Explorations, December 2003, Volume 5, Issue 2, pages 165-172.
• Neville, J., O. Simsek, D. Jensen, J. Komoroske, K. Palmer and H. Goldberg (2005). Using Relational Knowledge Discovery to Prevent Securities Fraud. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

• Tversky, A., and D. Kahneman (1974). Judgment under uncertainty: heuristics and biases. Science 185:1124-1131.
• Dawes, R., D. Faust, and P. Meehl (1989). Clinical versus actuarial judgment. Science 243:1668-1674.
• Background: Hand, D., H. Mannila, and P. Smyth (2001). Principles of Data Mining. MIT Press.
  • Chapter 4: Data Analysis and Uncertainty (skip 4.5.3)

• Hand, D., H. Mannila, and P. Smyth (2001). Principles of Data Mining. MIT Press.
  • Chapter 1: Introduction
  • Chapter 5: A Systematic Overview of Data Mining Algorithms (skip 5.3.1-5.3.3)
  • Chapter 10: Predictive Modeling for Classification (skip 10.3-10.4, 10.6-10.7, 10.9)

• Flach, P. and N. Lachiche (2004). Naive Bayesian classification of structured data. Machine Learning, 57(3): 233--269.
• Neville, J., D. Jensen, L. Friedland and M. Hay (2003). Learning Relational Probability Trees. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

• Popescul, A., L. Ungar, S. Lawrence, D. Pennock (2003). Statistical Relational Learning for Document Mining. In Proceedings of IEEE International Conference on Data Mining (ICDM 2003).
• Perlich, C., and F. Provost (2006). ACORA: Distribution-based Aggregation for Relational Learning from Identifier Attributes. Machine Learning, 62 (1/2) 65-105.

• E. Charniak (1991). Bayesian Networks without Tears. AI magazine.
• Koller, D., N. Friedman, L. Getoor, and B. Taskar (2007). Graphical Models in a Nutshell. Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, to appear.

• Rodrigo Braz, First-order Probabilistic Inference

• Getoor, L., N. Friedman, D. Koller, and A. Pfeffer (2001). Learning Probabilistic Relational Models. Relational Data Mining, S. Dzeroski and N. Lavrac, Eds., Springer-Verlag.

• Taskar, B., P. Abbeel, and D. Koller (2002). Relational Markov Networks. Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, to appear.

• Neville, J., and D. Jensen (2007). Relational Dependency Networks. Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, to appear.

• Dzeroski, S. (2007). Inductive Logic Programming in a Nutshell. Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, to appear.

• Kersting, K., and L. De Raedt (2007). Bayesian Logic Programming: Theory and Tools. Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, to appear.

• Richardson, M., and P. Domingos (2006). Markov Logic Networks. Machine Learning, 62, 107-136.

• Heckerman, D., C. Meek, and D. Koller (2004). Probabilistic Models for Relational Data. Microsoft Research Technical Report, MSR-TR-2004-30.
• Optional:
  Milch, B., B. Marthi, S. Russell, D. Sontag, D. Ong, and A. Kolobov (2007). BLOG: Probabilistic Models with Unknown Objects. Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, to appear.

• Jensen, D. and J. Neville (2002). Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. In Proceedings of the 19th International Conference on Machine Learning.
• Jensen, D., J. Neville and M. Hay (2003). Avoiding Bias When Aggregating Relational Data with Degree Disparity. In Proceedings of the 20th International Conference on Machine Learning.

• Macskassy, S. and F. Provost (2006). Classification in Networked Data: A toolkit and a univariate case study. Journal of Machine Learning Research, forthcoming.
• Optional:
  Taskar, B. V. Chatalbashev and D. Koller (2004). Learning Associative Markov Networks. In Proceedings of the 21st International Conference on Machine Learning (ICML04).

• Milch, B., B. Marthi, D. Sontag, S. Russell, D. Ong, and A. Kolobov (2005). Approximate Inference for Infinite Contingent Bayesian Networks. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS).
• Optional:
  Milch, B., and S. Russell (2006). General-Purpose MCMC Inference over Relational Structures. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI).

• C. Anderson, P. Domingos, and D. Weld (2002). Relational Markov models and their application to adaptive Web navigation. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining.
• Optional:
  Sanghai, S., P. Domingos, and D. Weld (2003). Dynamic Probabilistic Relational Models. In Proceedings of the 18th International Joint Conference on Artificial Intelligence.

• Kubica, J., A. Moore, J. Schneider, and Y. Yang (2002). Stochastic link and group detection. In Proceedings of the 18th National Conference on Artificial Intelligence, pages 798--804.
• Optional:
  Neville, J. and D. Jensen (2005). Leveraging Relational Autocorrelation with Latent Group Models. In Proceedings of the Fifth IEEE International Conference on Data Mining.

• Taskar, B., Abbeel, P., Wong, M., and Koller, D. (2003). Label and Link Prediction in Relational Data. In Advances in Neural Information Processing Systems (NIPS) 16.
• Optional:
  Liben-Nowell, D. and Kleinberg, J. (2003). The Link Prediction Problem for Social Networks. In Proceedings of the 12th International Conference on Information and Knowledge Management (CIKM).

• Pasula, H., B. Marthi, B. Milch, S. Russell and I. Shpitser (2003). Identity uncertainty and citation matching. In NIPS 15.
• Optional:
  Culotta, A. and A. McCallum (2005). Joint deduplication of multiple record types in relational data. In Proceedings of the 14th Conference on Information and Knowledge Management.

• Cook, D. and L. Holder (2000). Graph-Based Data Mining. IEEE Intelligent Systems, 15(2), pages 32-41.
• Optional:
  Dehaspe, L., H. Toivonen, R. King (1998). Finding frequent substructures in chemical compounds. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining.

• Presentations: Duncan, Rich, Josh & Rajesh

• Presentations: Don, Ferit, Umang, Chris

• **Project Final Report due April 26 midnight