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

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Author [ Title(Asc)] Year
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L
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, 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 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)
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)
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)
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)
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)
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)
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)
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)
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. 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)
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)
J
D. Sridhar and Getoor, L., “Joint Probabilistic Inference of Causal Structure”, in KDD Workshop on CD, 2016.PDF icon sridhar-cd16.pdf (204.51 KB)
D. Sridhar, Foulds, J., Walker, M., Huang, B., and Getoor, L., “Joint Models of Disagreement and Stance in Online Debate”, in Annual Meeting of the Association for Computational Linguistics (ACL), 2015.PDF icon sridhar-acl15.pdf (227.14 KB)
J. Pujara, Miao, H., and Getoor, L., “Joint Judgments with a Budget: Strategies for Reducing the Cost of Inference”, in ICML Workshop on Machine Learning with Test-Time Budgets, 2013.PDF icon pujara_wtbudg13.pdf (221.26 KB)
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)
L. Getoor and Taskar, B., Introduction to Statistical Relational Learning. The MIT Press, 2007.
L. Getoor, “An Introduction to Probabilistic Graphical Models for Relational Data”, Data Engineering Bulletin, vol. 29, 2006.
A. Ramesh, Goldwasser, D., Huang, B., Daume, III, H., and Getoor, L., “Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields”, IEEE Transactions on Learning Technologies (TLT), vol. 14, pp. 1-1, 2019.PDF icon ramesh-tlt19.pdf (4.3 MB)
H. Kang, Getoor, L., Shneiderman, B., Bilgic, M., and Licamele, L., “Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation”, IEEE Transactions on Visualization and Computer Graphics, vol. 14, pp. 999–1014, 2008.PDF icon kang-tvcg08.pdf (3.63 MB)
M. Bilgic, “Information Acquisition in Structured Domains”, University of Maryland - College Park, 2010.PDF icon mbilgic-phdthesis.pdf (4.68 MB)
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)
L. Licamele and Getoor, L., “Indirect two-sided relative ranking: a robust similarity measure for gene expression data”, BMC Bioinformatics, 2010.
K. Schnaitter, Polyzotis, N., and Getoor, L., “Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications”, in International Conference on Very Large Data Bases, 2009.PDF icon schnaitter-vldb09.pdf (743.29 KB)
L. Singh and Getoor, L., “Increasing the predictive power of affiliation networks.”, IEEE Data Engineering Bulletin, vol. 30, 2007.PDF icon singh.pdf (87.94 KB)
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)
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)
G. Mark Namata, “Identifying Graphs from Noisy Observational Data”, University of Maryland - College Park, 2012.PDF icon namata-phdthesis.pdf (1.51 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)
S. Srinivasan, Rao, N. S., Subbaian, K., and Getoor, L., “Identifying Facet Mismatches In Search Via Micrographs”, in International Conference on Information and Knowledge Management (CIKM), 2019.PDF icon srinivasan-cikm19.pdf (887.06 KB)
H
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)
P. Kouki, Fakhraei, S., Foulds, J., Eirinaki, M., and Getoor, L., “HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems”, in 9th ACM Conference on Recommender Systems (RecSys), 2015.PDF icon kouki-recsys15.pdf (1.03 MB)
S. H. Bach, Huang, B., London, B., and Getoor, L., “Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction”, in Uncertainty in Artificial Intelligence, 2013.PDF icon bach-uai13.pdf (379.45 KB)
S. H. Bach, “Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction”, University of Maryland, College Park, 2015.PDF icon bach-thesis15.pdf (1.17 MB)
S. H. Bach, Broecheler, M., Huang, B., and Getoor, L., “Hinge-Loss Markov Random Fields and Probabilistic Soft Logic”, Journal of Machine Learning Research (JMLR), vol. 18, pp. 1-67, 2017.PDF icon bach-jmlr17.pdf (731.56 KB)
S. H. Bach, Broecheler, M., Huang, B., and 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)
E. Zheleva, Getoor, L., and Sarawagi, S., “Higher-order Graphical Models for Classification in Social and Affiliation Networks”, in NIPS Workshop on Networks Across Disciplines: Theory and Applications, 2010.PDF icon zheleva-nips2010.pdf (200.43 KB)
X. He, Rekatsinas, T., Foulds, J., Getoor, L., and Liu, Y., “HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades”, in International Conference on Machine Learning, 2015.PDF icon He2015HawkesTopic.pdf (819.91 KB)
O. Udrea, Miller, R., and Getoor, L., “HOMER: Ontology visualization and analysis”, in Demo Presentation at International Semantic Web Conference (ISWC), 2007.PDF icon homer.pdf (125.38 KB)
O. Udrea, Getoor, L., and Miller, R., “HOMER: Ontology Alignment Visualization and Analysis”. 2007.PDF icon getoor-homer07.pdf (125.38 KB)
G
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)
B. Saha and Getoor, L., “Group Proximity Measure for Recommending Groups in Online Social Networks”, in 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD), 2008.PDF icon kddw-saha.pdf (311.36 KB)
H. Sharara and Getoor, L., “Group Detection”, Encyclopedia of Machine Learning, 2010.
D. Koller, Friedman, N., Getoor, L., and Taskar, B., Graphical Models in a Nutshell, 1st ed., vol. 1. MIT Press, 2007, p. 13--55.PDF icon koller-book07.pdf (513.11 KB)
A. Deshpande, Getoor, L., and Sen, P., Graphical Models for Uncertain Data, 1st ed., vol. 1. Springer, 2009, p. 1--34.PDF icon deshpande-book09.pdf (570.7 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)

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