Publications

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London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayes Generalization Bounds for Randomized Structured Prediction. NIP Workshop on Perturbation, Optimization and Statistics (2013).PDF icon london-nips13ws.pdf (205.57 KB)
London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayesian Collective Stability. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (2014).PDF icon london-aistats14.pdf (490.14 KB)
Getoor, L. & Grant, J. PRL: A Logical Approach to Probabilistic Relational Models. Machine Learning Journal 62, (2006).PDF icon getoor-mlj06.pdf (685.04 KB)
Hung, E., Getoor, L. & Subrahmanian, V. S. PXML: A Probabilistic Semistructured Data Model and Algebra. Proceedings of the IEEE International Conference on Data Engineering (2003).
Bach, S. H., Huang, B., Boyd-Graber, J. & Getoor, L. Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. International Conference on Machine Learning (ICML) (2015).PDF icon bach-icml15.pdf (356.46 KB)
Elsayed, T., Oard, D., Namata, G. Mark & Getoor, L. Personal Name Resolution in Email: A Heuristic Approach. (2008).PDF icon LAMP_150.pdf (397.61 KB)
Kouki, P., Schaffer, J., Pujara, J., ODonovan, J. & Getoor, L. Personalized Explanations for Hybrid Recommender Systems. Intelligent User Interfaces (2019).PDF icon kouki-iui19.pdf (3.34 MB)
Namata, G. Mark & Getoor, L. A Pipeline Approach to Graph Identification. Seventh International Workshop on Mining and Learning with Graphs (2009).PDF icon namatag-mlg09.pdf (93.77 KB)
Sen, P., Deshpande, A. & Getoor, L. PrDB: Managing and Exploiting Rich Correlations in Probabilistic Databases. VLDB Journal, special issue on uncertain and probabilistic databases (2009).PDF icon sen-vldbj09.pdf (1.12 MB)
Lansky, A. & Getoor, L. Practical Planning in COLLAGE. Proceedings of the AAAI Fall Symposium on Planning and Learning: On to Real Applications (1994).
Rastegari, M., Choi, J., Fakhraei, S., III, H. Daume & Davis, L. Predictable Dual-View Hashing. Proceedings of the 30th International Conference on Machine Learning (ICML-13) 1328–1336 (JMLR, 2013).PDF icon rastegari13.pdf (2.35 MB)
Tomkins, S., Ramesh, A. & Getoor, L. Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study. International Conference on Educational Data Mining (EDM) (2016).PDF icon tomkins-edm16.pdf (619.77 KB)
Licamele, L. & Getoor, L. Predicting Protein-Protein Interactions Using Relational Features. ICML Workshop on Statistical Network Analysis (2006).
Zheleva, E. Prediction, Evolution and Privacy in Social and Affiliation Networks. (2011).PDF icon zheleva-phdthesis11.pdf (5.81 MB)
Zheleva, E. & Getoor, L. Preserving the Privacy of Sensitive Relationships in Graph Data. Proceedings of the First SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007) 4890, 153-171 (Springer, 2008).
Zheleva, E. & Getoor, L. Preserving the Privacy of Sensitive Relationships in Graph Data. First ACM SIGKDD Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007) (2007).PDF icon zheleva-pinkdd07.pdf (373.86 KB)
Zheleva, E., Terzi, E. & Getoor, L. Privacy in Social Networks. (Morgan & Claypool Publishers, 2012).
Zheleva, E. & Getoor, L. Social Network Data Analytics (Aggarwal, C.) 247–276 (Springer, 2011).
Sridhar, D., Fakhraei, S. & Getoor, L. A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction. Bioinformatics (2016).PDF icon sridhar-bioinformatics_2016.pdf (1.94 MB)
Plangprasopchok, A., Lerman, K. & Getoor, L. A Probabilistic Approach for Learning Folksonomies from Structured Data. Fourth ACM International Conference on Web Search and Data Mining (WSDM) (2011).
Ramesh, A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. (2016).PDF icon ramesh-thesis16.pdf (865.41 KB)
Sridhar, D. & Getoor, L. Probabilistic Inference for Causal Structure Discovery. Uncertainty in Artificial Intelligence (UAI) Workshop on Causation (2016).PDF icon sridhar-uai-open-problem-v2.pdf (118.31 KB)
Hung, E., Getoor, L. & Subrahmanian, V. S. Probabilistic Interval XML. ACM Transactions on Computational Logic (TOCL) (2007).
Hung, E., Getoor, L. & Subrahmanian, V. S. Probabilistic Interval XML. Proceedings of the International Conference on Database Theory (2003).
Pujara, J. Probabilistic Models for Scalable Knowledge Graph Construction. (2016).PDF icon pujara-thesis15.pdf (1.06 MB)
Getoor, L., Segal, E., Taskar, B. & Koller, D. Probabilistic Models of Text and Link Structure for Hypertext Classification. IJCAI Workshop on Text Learning: Beyond Supervision (2001).PDF icon ijcai01-ws.pdf (127.03 KB)
Getoor, L., Friedman, N., Koller, D., Pfeffer, A. & Taskar, B. An Introduction to Statistical Relational Learning (Getoor, L. & Taskar, B.) (MIT Press, 2007).PDF icon srlbook-ch5.pdf (648.15 KB)
Broecheler, M., Mihalkova, L. & Getoor, L. Probabilistic Similarity Logic. Conference on Uncertainty in Artificial Intelligence (2010).PDF icon broecheler-uai10.pdf (399.54 KB)
Broecheler, M. & Getoor, L. Probabilistic Similarity Logic. International Workshop on Statistical Relational Learning (SRL'09) (2009).PDF icon broecheler-srl09.pdf (176.13 KB)
Bach, S. H., Huang, B. & Getoor, L. Probabilistic Soft Logic for Social Good. KDD Workshop on Data Science for Social Good (2014).PDF icon bach-dssg14.pdf (124.88 KB)
Huang, B., Kimmig, A., Getoor, L. & Golbeck, J. Probabilistic Soft Logic for Trust Analysis in Social Networks. International Workshop on Statistical Relational Artificial Intelligence (StaRAI 2012) (2012).PDF icon huang-starai12.pdf (241.96 KB)
Kim, S., Kini, N., Pujara, J., Koh, E. & Getoor, L. Probabilistic Visitor Stitching on Cross-Device Web Logs. International Conference on World Wide Web (WWW) 1581–1589 (2017).PDF icon p1581-kimwww17.pdf (1.23 MB)
Getoor, L. & Scheffer, T. Proceedings of the 28th International Conference on Machine Learning. Proceedings of the 28th International Conference on Machine Learning (2011).
Singh, L., Getoor, L. & Licamele, L. Pruning Social Networks Using Structural Properties and Descriptive Attributes. IEEE International Conference on Data Mining (ICDM) 773-776 (2005).PDF icon singh_icdm05.pdf (149.45 KB)