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Zheleva, E. Prediction, Evolution and Privacy in Social and Affiliation Networks. (2011).PDF icon zheleva-phdthesis11.pdf (5.81 MB)
Licamele, L. & Getoor, L. Predicting Protein-Protein Interactions Using Relational Features. ICML Workshop on Statistical Network Analysis (2006).
Tomkins, S., Ramesh, A. & Getoor, L. Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study. EDM (2016).PDF icon tomkins-edm16.pdf (619.77 KB)
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)
Lansky, A. & Getoor, L. Practical Planning in COLLAGE. Proceedings of the AAAI Fall Symposium on Planning and Learning: On to Real Applications (1994).
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)
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)
Kouki, P., Schaffer, J., Pujara, J., Odonovan, J. & Getoor, L. Personalized Explanations for Hybrid Recommender Systems. IUI (2019).PDF icon kouki-iui19.pdf (3.34 MB)
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)
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)
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).
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)
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)
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)
Hwang, H., Lauw, H., Getoor, L. & Ntoulas, A. Organizing User Search Histories. IEEE Transactions on Knowledge and Data Engineering (2010).
Somasundaran, S., Namata, G. Mark, Getoor, L. & Wiebe, J. Opinion Graphs for Polarity and Discourse Classification. TextGraphs-4: Graph-based Methods for Natural Language Processing (2009).PDF icon somasundaran-textgraphs09.pdf (289.75 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Ontology-Aware Partitioning for Knowledge Graph Identification. CIKM Workshop on Automatic Knowledge Base Construction (2013).PDF icon pujara_akbc13.pdf (370.62 KB)
Getoor, L. & Fromherz, M. Online Scheduling for Reprographic Machines. Working notes AAAI Workshop on Online Search (1997).
Pujara, J., London, B., Getoor, L. & Cohen, W. Online Inference for Knowledge Graph Construction. Workshop on Statistical Relational AI (2015).PDF icon pujara-starai15.pdf (340.95 KB)
Bhattacharya, I. & Getoor, L. Online Collective Entity Resolution. The 22nd National Conference on Artificial Intelligence (NECTAR Track) (AAAI Press, 2007).PDF icon nectar07.pdf (395.24 KB)
Ramesh, A., Rodriguez, M. & Getoor, L. Multi-relational influence models for online professional networks. International Conference on Web Intelligence (ICWI) 291-298 (ACM, 2017).PDF icon ramesh-icwi17.pdf (761.17 KB)
London, B., Rekatsinas, T., Huang, B. & Getoor, L. Multi-relational Weighted Tensor Decomposition. NIPS Workshop on SL (2012).PDF icon london-sl12.pdf (326.3 KB)
London, B., Rekatsinas, T., Huang, B. & Getoor, L. Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss. (2013).PDF icon mrwtd.pdf (460.45 KB)
Getoor, L. Multi-relational Data Mining Using Probabilistic Models. Multi-Relational Data Mining Workshop (2001).PDF icon mrdm.pdf (109.57 KB)
Sharara, H., Halgin, D., Getoor, L. & Borgatti, S. Multi-dimensional Trajectory Analysis for Career Histories. International Sunbelt Social Networks Conference (Sunbelt XXXI) (2011).
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic. NIPS Workshop on Data Driven Education (2013).PDF icon ramesh-nipsws13.pdf (153.92 KB)
Saha, B. & Getoor, L. On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch. 2009 SIAM International Conference on Data Mining (SDM09) (2009).PDF icon saha-sdm08.pdf (233.12 KB)
Augustine, E. & Farnadi, G. MLTrain: Collective Reasoning With Probabilistic Soft Logic. (2018). at <>PDF icon MLTrain - UAI 2018.pdf (8.93 MB)
Rekatsinas, T., Deshpande, A. & Getoor, L. Local Structure and Determinism in Probabilistic Databases. SIGMOD (2012).PDF icon rekatsinas-sigmod12.pdf (490.28 KB)
Lu, Q. & Getoor, L. Link-based Text Classification. IJCAI Workshop on "Text Mining and Link Analysis" (2003).PDF icon ijcai03-ws.pdf (97.25 KB)
Lu, Q. & Getoor, L. Link-based Classification Using Labeled and Unlabeled Data. ICML Workshop on "The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining (2003).PDF icon icml03-ws.pdf (274.65 KB)
Sen, P. & Getoor, L. Link-based Classification. (2007).PDF icon senum-tr07.pdf (511.11 KB)
Getoor, L. Link-based Classification. Advanced Methods for Knowledge Discovery from Complex Data 1, 189--207 (Springer-Verlag, 2005).PDF icon getoor-book05.pdf (273.43 KB)
Lu, Q. & Getoor, L. Link-based Classification. Proceedings of the International Conference on Machine Learning (ICML) (2003).PDF icon lu-icml03.pdf (195.81 KB)
Bilgic, M. & Getoor, L. Link-based Active Learning. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).PDF icon mbilgic-nips09wkshp.pdf (116.35 KB)
Namata, G. Mark & Getoor, L. Link Prediction. Encyclopedia of Machine Learning (2010).
Getoor, L. & Diehl, C. Link Mining: A Survey. SigKDD Explorations Special Issue on Link Mining 7, (2005).
Getoor, L. Link Mining: A New Data Mining Challenge. SIGKDD Explorations, volume 5, 85- -89 (2003).
Kimmig, A., Mihalkova, L. & Getoor, L. Lifted graphical models: a survey. Machine Learning 1-45 (2014).
Kimmig, A., Mihalkova, L. & Getoor, L. Lifted graphical models: a survey. Machine Learning Journal 99, 1–45 (2015).PDF icon kimmig-mlj15.pdf (785.58 KB)
Srinivasan, S., Babaki, B., Farnadi, G. & Getoor, L. Lifted Hinge-Loss Markov Random Fields. AAAI (2019).PDF icon srinivasan-aaai19.pdf (417.5 KB)
Mihalkova, L. & Getoor, L. Lifted Graphical Models: A Survey. (2011).PDF icon 1107.4966v2.pdf (446.54 KB)
Smith, M., Barash, V., Getoor, L. & Lauw, H. Leveraging Social Context for Searching Social Media. CIKM Workshop on Search in Social Media (2008).
Udrea, O., Getoor, L. & Miller, R. Leveraging Data and Structure in Ontology Integration. Proceedings of ACM-SIGMOD 2007 International Conference on Management 449–460 (2007).PDF icon p449.pdf (509.48 KB)
Mihalkova, L., Moustafa, W. Eldin & Getoor, L. Learning to Predict Web Collaborations. WSDM Workshop on User Modeling for Web Applications (2011).PDF icon mihalkova-wikiCollabs.pdf (353.9 KB)
Getoor, L., Friedman, N. & Koller, D. Learning Structured Statistical Models from Relational Data. Electronic Transactions on Artificial Intelligence 6, (2002).
Getoor, L. Learning Statistical Models from Relational Data. (2001).PDF icon getoor-thesis.pdf (3.39 MB)
Getoor, L., Koller, D., Taskar, B. & Friedman, N. Learning Probabilistic Relational Models with Structural Uncertainty. Proceedings of the AAAI Workshop on Learning Statistical Models from Relational Data (2000).