Publications

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J. Pujara, Miao, H., Getoor, L., and Cohen, W., Ontology-Aware Partitioning for Knowledge Graph Identification, in CIKM Workshop on Automatic Knowledge Base Construction, 2013.PDF icon pujara_akbc13.pdf (370.62 KB)
J. Pujara and Skomoroch, P., Large-Scale Hierarchical Topic Models, in NIPS Workshop on BigLearn, 2012.PDF icon pujara_biglearn12.pdf (189.96 KB)
J. Pujara, London, B., and Getoor, L., Reducing Label Cost by Combining Feature Labels and Crowdsourcing, in ICML Workshop on Combining Learning Strategies to Reduce Label Cost, 2011.PDF icon clsicml_pujara_london.pdf (253.29 KB)
J. Pujara, III, H. Daume, and Getoor, L., Using Classifier Cascades for Scalable E-Mail Classification, in Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, 2011.PDF icon pujara_ceas2011_camera.pdf (308.42 KB)
J. Pujara and Getoor, L., Coarse-to-Fine, Cost-Sensitive Classification of E-Mail, in NIPS Workshop on Coarse-to-Fine Processing, 2010.PDF icon pujara_nips10.pdf (258.86 KB)
J. Pujara, Augustine, E., and Getoor, L., Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short, in Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.PDF icon pujara-emnlp17.pdf (677.74 KB)
J. Pujara, London, B., and Getoor, L., Budgeted Online Collective Inference, in UAI, 2015.PDF icon pujara-uai15.pdf (302.63 KB)
J. Pujara, London, B., Getoor, L., and Cohen, W., Online Inference for Knowledge Graph Construction., in Workshop on Statistical Relational AI, 2015.PDF icon pujara-starai15.pdf (340.95 KB)
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Using Semantics & Statistics to Turn Data into Knowledge, AI Magazine, vol. 36, pp. 65–74, 2015.PDF icon pujara_aimag15.pdf (359.48 KB)
J. Pujara, Probabilistic Models for Scalable Knowledge Graph Construction, University of Maryland, College Park, 2016.PDF icon pujara-thesis15.pdf (1.06 MB)
J. Pujara and Getoor, L., Generic Statistical Relational Entity Resolution in Knowledge Graphs, in StarAI, 2016.PDF icon pujara-starai16.pdf (151.37 KB)
J. Pujara and Getoor, L., Building Dynamic Knowledge Graphs, in NIPS Workshop on Automated Knowledge Base Construction, 2014.PDF icon pujara_akbc14.pdf (143.26 KB)
M. Polymeropoulos, Licamele, L., Volpi, S., Mack, K., Mitkus, S., Carstea, E., Getoor, L., and Lavedan, C., Common effect of antipsychotics on the biosynthesis and regulation of fatty acids and cholesterol supports a key role of lipid homeostasis in schizophrenia., Schizophrenia Research, 2009.
A. Plangprasopchok, Lerman, K., and Getoor, L., A Probabilistic Approach for Learning Folksonomies from Structured Data, in Fourth ACM International Conference on Web Search and Data Mining (WSDM), 2011.
A. Plangprasopchok, Lerman, K., and Getoor, L., Growing a tree in the forest: constructing folksonomies by integrating structured metadata, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.PDF icon plang-kdd10.pdf (705.71 KB)
G. Piatetsky-Shapiro, Grossman, R., Djeraba, C., Feldman, R., Getoor, L., and Zaki, M., Is there a grand challenge or X-prize for data mining?, in 12th International Conference on Knowledge Discovery and Data Mining, 2006.
P. Panagiotis, Panayiotis, T., Ariel, F., and Getoor, L., TACI: Taxonomy-Aware Catalog Integration, TKDE, vol. 25, 2012.PDF icon papadimitriou-tkde12.pdf (2.93 MB)
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G. Mark Namata, Identifying Graphs from Noisy Observational Data, University of Maryland - College Park, 2012.PDF icon namata-phdthesis.pdf (1.51 MB)
G. Namata, London, B., Getoor, L., and Huang, B., Query-driven Active Surveying for Collective Classification, in ICML Workshop on MLG, 2012.PDF icon namata-mlg12.pdf (257.49 KB)
G. Namata, Kok, S., and Getoor, L., Collective Graph Identification, in KDD, 2011.PDF icon namata-kdd11.pdf (185.7 KB)
G. Namata, Sharara, H., and Getoor, L., A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks, 1st ed., vol. 1. Springer, 2010, p. 107--133.PDF icon namata-book10.pdf (656.83 KB)
G. Mark Namata and Getoor, L., Link Prediction, Encyclopedia of Machine Learning, 2010.
G. Mark Namata and Getoor, L., A Pipeline Approach to Graph Identification, in Seventh International Workshop on Mining and Learning with Graphs, 2009.PDF icon namatag-mlg09.pdf (93.77 KB)
G. Namata, Sen, P., Bilgic, M., and Getoor, L., Collective Classification for Text Classification, 1st ed., vol. 1. Taylor and Francis Group, 2009, p. 51--69.PDF icon namata-book09.pdf (4.35 MB)
G. Mark Namata and Getoor, L., Identifying Graphs From Noisy and Incomplete Data, in 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, 2009.PDF icon namatag-kddu09.pdf (241.7 KB)
G. Mark Namata, Staats, B., Getoor, L., and Shneiderman, B., A Dual-View Approach to Interactive Network Visualization, in ACM Conference on Information and Knowledge Management, 2007.PDF icon cikm0671-namata.pdf (376.54 KB)
G. Mark Namata, Getoor, L., and Diehl, C., Inferring Organizational Titles in Online Communications, in ICML Workshop on Statistical Network Analysis, 2006.PDF icon icml2006_ExtAbst.pdf (72.39 KB)
G. Namata, London, B., and Getoor, L., Collective Graph Identification, TKDD, vol. 10, 2015.PDF icon namata-tkdd15.pdf (500.96 KB)
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S. Muthiah, Huang, B., Arredondo, J., Mares, D., Getoor, L., Katz, G., and Ramakrishnan, N., Capturing Planned Protests from Open Source Indicators, AI Mag, vol. 37, pp. 63–75, 2016.PDF icon muthiah-aimag16.pdf (1.23 MB)
W. Eldin Moustafa, Kimmig, A., Deshpande, A., and Getoor, L., Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty, in International Conference on Data Engineering (ICDE), 2014.PDF icon ICDE14_conf_full_374.pdf (1.57 MB)
W. Moustafa, Miao, H., Deshpande, A., and Getoor, L., GrDB: A System for Declarative and Interactive Analysis of Noisy Information Networks, in SIGMOD, 2013.PDF icon moustafa-sigmod13.pdf (1.1 MB)
W. Eldin Moustafa, Deshpande, A., and Getoor, L., Ego-centric Graph Pattern Census, in International Conference on Data Engineering (ICDE), 2012.PDF icon moustafaicde.pdf (2.27 MB)
W. Moustafa, Namata, G., Deshpande, A., and Getoor, L., Declarative Analysis of Noisy Information Networks, in ICDE Workshop on GDM, 2011.PDF icon moustafa-gdm11.pdf (1.55 MB)
S. Minton, Michelson, M., See, K., Macskassy, S., Gazen, B. C., and Getoor, L., Improving Classifier Performance by Autonomously Collecting Background Knowledge from the Web, in Tenth International Conference on Machine Learning and Applications, 2011.PDF icon minton-icmla2011.pdf (733.09 KB)
L. Mihalkova, Moustafa, W. Eldin, and Getoor, L., Learning to Predict Web Collaborations, in WSDM Workshop on User Modeling for Web Applications, 2011.PDF icon mihalkova-wikiCollabs.pdf (353.9 KB)
L. Mihalkova and Getoor, L., Lifted Graphical Models: A Survey. 2011.PDF icon 1107.4966v2.pdf (446.54 KB)
H. Miao, Liu, X., Huang, B., and Getoor, L., A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization, in 2013 IEEE International Conference on Big Data, 2013.PDF icon miao-bd13.pdf (307.51 KB)
A. Memory, Kimmig, A., Bach, S. H., Raschid, L., and Getoor, L., Graph Summarization in Annotated Data Using Probabilistic Soft Logic, in Proceedings of the International Workshop on Uncertainty Reasoning for the Semantic Web (URSW), 2012.PDF icon mrc_iswc12_ws.pdf (411.71 KB)
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Q. Lu and Getoor, L., Link-based Classification, in Proceedings of the International Conference on Machine Learning (ICML), 2003.PDF icon lu-icml03.pdf (195.81 KB)
Q. Lu and Getoor, L., Link-based Classification Using Labeled and Unlabeled Data, in 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)
Q. Lu and Getoor, L., Link-based Text Classification, in IJCAI Workshop on "Text Mining and Link Analysis", 2003.PDF icon ijcai03-ws.pdf (97.25 KB)
B. London, Huang, B., Taskar, B., and Getoor, L., PAC-Bayesian Collective Stability, in Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, 2014.PDF icon london-aistats14.pdf (490.14 KB)
B. London, Khamis, S., Bach, S., Huang, B., Getoor, L., and Davis, L., Collective Activity Detection using Hinge-loss Markov Random Fields, in CVPR Workshop on SPTLE, 2013.PDF icon london-sptle13.pdf (705.87 KB)
B. London and Getoor, L., Collective Classification of Network Data, 1st ed., vol. 1. CRC Press, 2013, p. 399--416.PDF icon london-book13.pdf (394.37 KB)
B. London, Huang, B., Taskar, B., and Getoor, L., Collective Stability in Structured Prediction: Generalization from One Example, in ICML, 2013.PDF icon london-icml13.pdf (373.82 KB)
B. London, Huang, B., and Getoor, L., Graph-based Generalization Bounds for Learning Binary Relations. University of Maryland College Park, 2013.PDF icon br_risk_bounds.pdf (304.54 KB)
B. London, Rekatsinas, T., Huang, B., and Getoor, L., Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss. University of Maryland College Park, 2013.PDF icon mrwtd.pdf (460.45 KB)
B. London, Huang, B., Taskar, B., and Getoor, L., PAC-Bayes Generalization Bounds for Randomized Structured Prediction, in NIP Workshop on Perturbation, Optimization and Statistics, 2013.PDF icon london-nips13ws.pdf (205.57 KB)
B. London, Huang, B., and Getoor, L., Improved Generalization Bounds for Large-scale Structured Prediction, in NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Networks, 2012.PDF icon london-nips12ws.pdf (213.95 KB)
B. London, Rekatsinas, T., Huang, B., and Getoor, L., Multi-relational Weighted Tensor Decomposition, in NIPS Workshop on SL, 2012.PDF icon london-sl12.pdf (326.3 KB)

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