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L

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).

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 Articial Intelligence (1999).

icjai99.pdf (156.94 KB)

Getoor, L., Friedman, N., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. Relational Data Mining (Springer-Verlag, 2001).

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).

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).

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).

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).

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).

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).

Foulds2015LatentTopicNetworks.pdf (382.53 KB)

Bhattacharya, I. & Getoor, L. A Latent Dirichlet Model for Unsupervised Entity Resolution. SIAM Conference on Data Mining (SDM) (2006).

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).

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).

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).

pujara_s4bd13.pdf (306.96 KB)

Pujara, J. & Skomoroch, P. Large-Scale Hierarchical Topic Models. NIPS Workshop on Big Learning (2012).

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).

kang-sbp13.pdf (622.52 KB)

K

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Knowledge Graph Identification. International Semantic Web Conference (ISWC) (2013).

pujara_iswc13.pdf (508.7 KB)

J

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).

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).

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).

pujara_wtbudg13.pdf (221.26 KB)

I

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).

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).

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).

kang-tvcg08.pdf (3.63 MB)

Bilgic, M. Information Acquisition in Structured Domains. (2010).

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).

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).

schnaitter-vldb09.pdf (743.29 KB)

Singh, L. & Getoor, L. Increasing the predictive power of affiliation networks. IEEE Data Engineering Bulletin 30, (2007).

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).

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).

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).

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).

icdm_2018.pdf (473.58 KB)

Namata, G. Mark. Identifying Graphs from Noisy Observational Data. (2012).

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).

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).

srinivasan-cikm19.pdf (887.06 KB)

H

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).

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).

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).

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).

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).

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).

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).

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).

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).

homer.pdf (125.38 KB)

Udrea, O., Getoor, L. & Miller, R. HOMER: Ontology Alignment Visualization and Analysis. (2007).

getoor-homer07.pdf (125.38 KB)

G

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).

plang-kdd10.pdf (705.71 KB)

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