Archived Publications (Latest: https://linqs.github.io/linqs-website/publications/)

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I
I. Bhattacharya and Getoor, L., Iterative Record Linkage for Cleaning and Integration, in ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), 2004.PDF icon bhattacharyasigmod04-wkshp.pdf (222.38 KB)
K
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Knowledge Graph Identification, in International Semantic Web Conference (ISWC), 2013.PDF icon pujara_iswc13.pdf (508.7 KB)
L
J. Kang, Lerman, K., and Getoor, L., LA-LDA: A Limited Attention Topic Model for Social Recommendation, in The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013), 2013.PDF icon kang-sbp13.pdf (622.52 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, Miao, H., Getoor, L., and Cohen, W., Large-Scale Knowledge Graph Identification using PSL, in ICML Workshop on Structured Learning (SLG), 2013.PDF icon pujara_slg13.pdf (277.63 KB)
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Large-Scale Knowledge Graph Identification using PSL, in AAAI Fall Symposium on Semantics for Big Data, 2013.PDF icon pujara_s4bd13.pdf (306.96 KB)
S. H. Bach, Huang, B., and Getoor, L., Large-margin Structured Learning for Link Ranking, in NIPS Workshop on Frontiers of Network Analysis: Methods, Models, and Applications, 2013.PDF icon bach-fna13.pdf (210.09 KB)
I. Bhattacharya and Getoor, L., A Latent Dirichlet Model for Unsupervised Entity Resolution, in SIAM Conference on Data Mining (SDM), 2006.PDF icon bhattacharyasdm06.pdf (209.24 KB)
J. Foulds, Kumar, S., and Getoor, L., Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models, in International Conference on Machine Learning (ICML), 2015.PDF icon Foulds2015LatentTopicNetworks.pdf (382.53 KB)
J. Doppa, Yu, J., Tadepalli, P., and Getoor, L., Learning Algorithms for Link Prediction based on Chance Constraints, in European Conference on Machine Learning (ECML), 2010.PDF icon doppa-ecml10.pdf (203 KB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Learning Latent Engagement Patterns of Students in Online Courses, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.PDF icon ramesh-aaai14.pdf (505.47 KB)
S. H. Bach, Huang, B., and Getoor, L., Learning Latent Groups with Hinge-loss Markov Random Fields, in ICML Workshop on Inferning: Interactions between Inference and Learning, 2013.PDF icon bach-inferning13.pdf (348.79 KB)
L. Getoor, Friedman, N., Koller, D., and Taskar, B., Learning Probabilistic Models of Link Structure, Journal of Machine Learning Research, vol. 3, p. 679- -707, 2002.PDF icon jmlr02.pdf (502.22 KB)
L. Getoor, Friedman, N., Koller, D., and Taskar, B., Learning Probabilistic Models of Relational Structure, in Proceedings of International Conference on Machine Learning (ICML), 2001.PDF icon icml01.pdf (157.91 KB)
L. Getoor, Friedman, N., Koller, D., and Pfeffer, A., Learning Probabilistic Relational Models, 1st ed., vol. 1. Springer-Verlag, 2001, p. 307--335.
N. Friedman, Getoor, L., Koller, D., and Pfeffer, A., Learning Probabilistic Relational Models, in International Joint Conference on Arti cial Intelligence, 1999.PDF icon icjai99.pdf (156.94 KB)
L. Getoor, Friedman, N., Koller, D., and Pfeffer, A., Learning Probabilistic Relational Models, in Relational Data Mining, 2001.PDF icon lprm-ch.pdf (376 KB)
L. Getoor, Koller, D., Taskar, B., and Friedman, N., Learning Probabilistic Relational Models with Structural Uncertainty, in Proceedings of the AAAI Workshop on Learning Statistical Models from Relational Data, 2000.
L. Getoor, Learning Statistical Models from Relational Data, Stanford, 2001.PDF icon getoor-thesis.pdf (3.39 MB)
L. Getoor, Friedman, N., and Koller, D., Learning Structured Statistical Models from Relational Data, Electronic Transactions on Artificial Intelligence, vol. 6, 2002.
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)
O. Udrea, Getoor, L., and Miller, R., Leveraging Data and Structure in Ontology Integration, in Proceedings of ACM-SIGMOD 2007 International Conference on Management, 2007, pp. 449–460.PDF icon p449.pdf (509.48 KB)
M. Smith, Barash, V., Getoor, L., and Lauw, H., Leveraging Social Context for Searching Social Media, in CIKM Workshop on Search in Social Media, 2008.
L. Mihalkova and Getoor, L., Lifted Graphical Models: A Survey. 2011.PDF icon 1107.4966v2.pdf (446.54 KB)
S. Srinivasan, Babaki, B., Farnadi, G., and Getoor, L., Lifted Hinge-Loss Markov Random Fields, in AAAI Conference on Artificial Intelligence (AAAI), 2019.PDF icon srinivasan-aaai19.pdf (417.5 KB)
A. Kimmig, Mihalkova, L., and Getoor, L., Lifted graphical models: a survey, Machine Learning, pp. 1-45, 2014.
A. Kimmig, Mihalkova, L., and Getoor, L., Lifted graphical models: a survey, Machine Learning Journal, vol. 99, pp. 1–45, 2015.PDF icon kimmig-mlj15.pdf (785.58 KB)
L. Getoor, Link Mining: A New Data Mining Challenge, SIGKDD Explorations, volume, vol. 5, p. 85- -89, 2003.
L. Getoor and Diehl, C., Link Mining: A Survey, SigKDD Explorations Special Issue on Link Mining, vol. 7, 2005.
G. Mark Namata and Getoor, L., Link Prediction, Encyclopedia of Machine Learning, 2010.
M. Bilgic and Getoor, L., Link-based Active Learning, in NIPS Workshop on Analyzing Networks and Learning with Graphs, 2009.PDF icon mbilgic-nips09wkshp.pdf (116.35 KB)
P. Sen and Getoor, L., Link-based Classification. University of Maryland, 2007.PDF icon senum-tr07.pdf (511.11 KB)
L. Getoor, Link-based Classification, 1st ed., vol. 1. Springer-Verlag, 2005, p. 189--207.PDF icon getoor-book05.pdf (273.43 KB)
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)
T. Rekatsinas, Deshpande, A., and Getoor, L., Local Structure and Determinism in Probabilistic Databases, in SIGMOD, 2012.PDF icon rekatsinas-sigmod12.pdf (490.28 KB)
M
E. Augustine and Farnadi, G., MLTrain: Collective Reasoning With Probabilistic Soft Logic. Uncertainty in Artificial Intelligence (UAI), 2018.PDF icon augustine-uai18.pdf (8.93 MB)
B. Saha and Getoor, L., On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch, in 2009 SIAM International Conference on Data Mining (SDM09), 2009.PDF icon saha-sdm08.pdf (233.12 KB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic, in NIPS Workshop on Data Driven Education, 2013.PDF icon ramesh-nipsws13.pdf (153.92 KB)
H. Sharara, Halgin, D., Getoor, L., and Borgatti, S., Multi-dimensional Trajectory Analysis for Career Histories, in International Sunbelt Social Networks Conference (Sunbelt XXXI), 2011.
L. Getoor, Multi-relational Data Mining Using Probabilistic Models, in Multi-Relational Data Mining Workshop, 2001.PDF icon mrdm.pdf (109.57 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, 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)
A. Ramesh, Rodriguez, M., and Getoor, L., Multi-relational influence models for online professional networks, in International Conference on Web Intelligence (ICWI), 2017, pp. 291-298.PDF icon ramesh-icwi17.pdf (761.17 KB)

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