Publications

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L
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).
Getoor, L., Friedman, N., Koller, D. & Pfeffer, A. Relational Data Mining (Dzeroski, S. & Lavrac, N.) (Springer-Verlag, 2001).
Friedman, N., Getoor, L., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. International Joint Conference on Arti cial Intelligence (1999).PDF icon icjai99.pdf (156.94 KB)
Getoor, L., Friedman, N., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. Relational Data Mining (Springer-Verlag, 2001).PDF icon lprm-ch.pdf (376 KB)
Getoor, L., Friedman, N., Koller, D. & Taskar, B. Learning Probabilistic Models of Relational Structure. Proceedings of International Conference on Machine Learning (ICML) (2001).PDF icon icml01.pdf (157.91 KB)
Getoor, L., Friedman, N., Koller, D. & Taskar, B. Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research 3, 679- -707 (2002).PDF icon jmlr02.pdf (502.22 KB)
Bach, S. H., Huang, B. & Getoor, L. Learning Latent Groups with Hinge-loss Markov Random Fields. ICML Workshop on Inferning: Interactions between Inference and Learning (2013).PDF icon bach-inferning13.pdf (348.79 KB)
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Learning Latent Engagement Patterns of Students in Online Courses. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI Press, 2014).PDF icon ramesh-aaai14.pdf (505.47 KB)
Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Learning Algorithms for Link Prediction based on Chance Constraints. European Conference on Machine Learning (ECML) (2010).PDF icon doppa-ecml10.pdf (203 KB)
Foulds, J., Kumar, S. & Getoor, L. Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models. International Conference on Machine Learning (ICML) (2015).PDF icon Foulds2015LatentTopicNetworks.pdf (382.53 KB)
Bhattacharya, I. & Getoor, L. A Latent Dirichlet Model for Unsupervised Entity Resolution. SIAM Conference on Data Mining (SDM) (2006).PDF icon bhattacharyasdm06.pdf (209.24 KB)
Bach, S. H., Huang, B. & Getoor, L. Large-margin Structured Learning for Link Ranking. NIPS Workshop on Frontiers of Network Analysis: Methods, Models, and Applications (2013).PDF icon bach-fna13.pdf (210.09 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Large-Scale Knowledge Graph Identification using PSL. ICML Workshop on Structured Learning (SLG) (2013).PDF icon pujara_slg13.pdf (277.63 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Large-Scale Knowledge Graph Identification using PSL. AAAI Fall Symposium on Semantics for Big Data (2013).PDF icon pujara_s4bd13.pdf (306.96 KB)
Pujara, J. & Skomoroch, P. Large-Scale Hierarchical Topic Models. NIPS Workshop on Big Learning (2012).PDF icon pujara_biglearn12.pdf (189.96 KB)
Kang, J., Lerman, K. & Getoor, L. LA-LDA: A Limited Attention Topic Model for Social Recommendation. The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013) (2013).PDF icon kang-sbp13.pdf (622.52 KB)
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Pujara, J., Miao, H., Getoor, L. & Cohen, W. Knowledge Graph Identification. International Semantic Web Conference (ISWC) (2013).PDF icon pujara_iswc13.pdf (508.7 KB)
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Sridhar, D. & Getoor, L. Joint Probabilistic Inference of Causal Structure. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop on Causal Discovery (2016).PDF icon sridhar-kdd-ws16.pdf (204.51 KB)
Sridhar, D., Foulds, J., Walker, M., Huang, B. & Getoor, L. Joint Models of Disagreement and Stance in Online Debate. Annual Meeting of the Association for Computational Linguistics (ACL) (2015).PDF icon sridhar-acl15.pdf (227.14 KB)
Pujara, J., Miao, H. & Getoor, L. Joint Judgments with a Budget: Strategies for Reducing the Cost of Inference. ICML Workshop on Machine Learning with Test-Time Budgets (2013).PDF icon pujara_wtbudg13.pdf (221.26 KB)
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Bhattacharya, I. & Getoor, L. Iterative Record Linkage for Cleaning and Integration. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD) (2004).PDF icon bhattacharyasigmod04-wkshp.pdf (222.38 KB)
Getoor, L. & Taskar, B. Introduction to Statistical Relational Learning. (The MIT Press, 2007).
Getoor, L. An Introduction to Probabilistic Graphical Models for Relational Data. Data Engineering Bulletin 29, (2006).
Ramesh, A., Goldwasser, D., Huang, B., Daume-III, H. & Getoor, L. Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields. Transactions on Learning Technologies (2019).PDF icon ramesh-tlt19.pdf (4.3 MB)
Kang, H., Getoor, L., Shneiderman, B., Bilgic, M. & Licamele, L. Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation. IEEE Transactions on Visualization and Computer Graphics 14, 999–1014 (2008).PDF icon kang-tvcg08.pdf (3.63 MB)
Bilgic, M. Information Acquisition in Structured Domains. (2010).PDF icon mbilgic-phdthesis.pdf (4.68 MB)
Namata, G. Mark, Getoor, L. & Diehl, C. Inferring Organizational Titles in Online Communications. ICML Workshop on Statistical Network Analysis (2006).PDF icon icml2006_ExtAbst.pdf (72.39 KB)
Licamele, L. & Getoor, L. Indirect two-sided relative ranking: a robust similarity measure for gene expression data. BMC Bioinformatics (2010).
Schnaitter, K., Polyzotis, N. & Getoor, L. Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications. International Conference on Very Large Data Bases (2009).PDF icon schnaitter-vldb09.pdf (743.29 KB)
Singh, L. & Getoor, L. Increasing the predictive power of affiliation networks. IEEE Data Engineering Bulletin 30, (2007).PDF icon singh.pdf (87.94 KB)
Minton, S. et al. Improving Classifier Performance by Autonomously Collecting Background Knowledge from the Web. Tenth International Conference on Machine Learning and Applications (2011).PDF icon minton-icmla2011.pdf (733.09 KB)
London, B., Huang, B. & Getoor, L. Improved Generalization Bounds for Large-scale Structured Prediction. NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Networks (2012).PDF icon london-nips12ws.pdf (213.95 KB)
Tomkins, S., Farnadi, G., Amantullah, B., Getoor, L. & Minton, S. The Impact of Environmental Stressors on Human Trafficking. Beyond Online Data (ICWSM Workshop) (2018).PDF icon icdm_2018.pdf (473.58 KB)
Tomkins, S., Farnadi, G., Amantullah, B., Getoor, L. & Minton, S. The Impact of Environmental Stressors on Human Trafficking. International Conference on Data Mining (ICDM) (2018).PDF icon icdm_2018.pdf (473.58 KB)
Namata, G. Mark. Identifying Graphs from Noisy Observational Data. (2012).PDF icon namata-phdthesis.pdf (1.51 MB)
Namata, G. Mark & Getoor, L. Identifying Graphs From Noisy and Incomplete Data. 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (2009).PDF icon namatag-kddu09.pdf (241.7 KB)
Srinivasan, S., Rao, N. S., Subbaian, K. & Getoor, L. Identifying Facet Mismatches In Search Via Micrographs. The 28th ACM International Conference on Information and Knowledge Management (2019).
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Miao, H., Liu, X., Huang, B. & Getoor, L. A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization. 2013 IEEE International Conference on Big Data (2013).PDF icon miao-bd13.pdf (307.51 KB)
Kouki, P., Fakhraei, S., Foulds, J., Eirinaki, M. & Getoor, L. HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems. 9th ACM Conference on Recommender Systems (RecSys) (ACM, 2015).PDF icon kouki-recsys15.pdf (1.03 MB)
Bach, S. H., Huang, B., London, B. & Getoor, L. Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. Uncertainty in Artificial Intelligence (2013).PDF icon bach-uai13.pdf (379.45 KB)
Bach, S. H. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction. (2015).PDF icon bach-thesis15.pdf (1.17 MB)
Bach, S. H., Broecheler, M., Huang, B. & Getoor, L. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. Journal of Machine Learning Research (JMLR) 18, 1-67 (2017).PDF icon bach-jmlr17.pdf (731.56 KB)
Bach, S. H., Broecheler, M., Huang, B. & Getoor, L. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. ArXiv:1505.04406 [cs.LG] (2015).PDF icon bach-arxiv15.pdf (686.27 KB)
Zheleva, E., Getoor, L. & Sarawagi, S. Higher-order Graphical Models for Classification in Social and Affiliation Networks. NIPS Workshop on Networks Across Disciplines: Theory and Applications (2010).PDF icon zheleva-nips2010.pdf (200.43 KB)
He, X., Rekatsinas, T., Foulds, J., Getoor, L. & Liu, Y. HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. International Conference on Machine Learning (2015).PDF icon He2015HawkesTopic.pdf (819.91 KB)
Udrea, O., Miller, R. & Getoor, L. HOMER: Ontology visualization and analysis. Demo Presentation at International Semantic Web Conference (ISWC) (2007).PDF icon homer.pdf (125.38 KB)
Udrea, O., Getoor, L. & Miller, R. HOMER: Ontology Alignment Visualization and Analysis. (2007).PDF icon getoor-homer07.pdf (125.38 KB)
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Plangprasopchok, A., Lerman, K. & Getoor, L. Growing a tree in the forest: constructing folksonomies by integrating structured metadata. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010).PDF icon plang-kdd10.pdf (705.71 KB)
Saha, B. & Getoor, L. Group Proximity Measure for Recommending Groups in Online Social Networks. 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD) (2008).PDF icon kddw-saha.pdf (311.36 KB)

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