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G. Farnadi, Babaki, B., and Getoor, L., Fairness in Relational Domains, in Artificial Intelligence, Ethics, and Society (AIES), 2018.PDF icon farnadi_aies2018.pdf (418.24 KB)
G. Farnadi, Babaki, B., and Getoor, L., Fairness-aware Relational Learning and Inference, in AAAI Workshop on Declarative Learning Based Programming (DeLBP), 2018.PDF icon farnadi-delbp2018.pdf (169.37 KB)
R. Islamaj, Getoor, L., and W. Wilbur, J., Feature Generation Algorithm: an Application to Splice Site Prediction, in Knowledge Discovery in Databases: PKDD 2006, Berlin, Germany, 2006, vol. 4213, pp. 553-560.PDF icon rezarta-pkdd.pdf (163.92 KB)
R. Islamaj, Getoor, L., and W. Wilbur, J., A Feature Generation Algorithm for Sequences with Application to Splice Site Prediction, in International Workshop on Feature Selection for Data Mining (FSDM), Bethesda, Maryland, 2006.
R. Islamaj, Getoor, L., W. Wilbur, J., and Mount, S., Features generated for computational splice-site prediction correspond to functional elements, BMC Bioinformatics, vol. 8, 2007.
H. Sharara, Singh, L., and Getoor, L., Finding Prominent Actors in Dynamic Affiliation Networks, Human Journal, 2012.PDF icon 105-204-1-SM.pdf (757.7 KB)
T. Rekatsinas, Dong, X. Luna, Getoor, L., and Srivastava, D., Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration, in 7th Biennial Conference on Innovative Data Systems Research (CIDR `15), 2015.PDF icon rekatsinasCIDR15.pdf (396.99 KB)
T. Rekatsinas, Ghosh, S., Mekaru, S., Nsoesie, E., Brownstein, J., Getoor, L., and Ramakrishnan, N., Forecasting Rare Disease Outbreaks Using Multiple Data Sources, STAT ANAL DATA MIN, 2015.
L. Getoor, Koller, D., and Friedman, N., From Instances to Classes in Probabilistic Relational Models, in Proceedings of the ICML Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries, 2000.
L. Getoor, Koller, D., and Friedman, N., From Instances to Classes in Probabilistic Relational Models, in Proceedings of the ICML Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries, 2000.
H. Sayyadi and Getoor, L., Future Rank: Ranking Scientific Articles by Predicting their Future PageRank, in 2009 SIAM International Conference on Data Mining (SDM09), 2009.PDF icon sayyadi_futureRank_sdm09.pdf (621.55 KB)
H. Sharara, Sopan, A., Namata, G. Mark, Getoor, L., and Singh, L., G-PARE: A Visual Analytic Tool for Comparative Analysis of Uncertain Graphs, in IEEE Conference on Visual Analytics Science and Technology (VAST), 2011.PDF icon sharara-vast11.pdf (1.64 MB)
P. Kouki, Schaffer, J., Pujara, J., O'Donovan, J., and Getoor, L., Generating and Understanding Personalized Explanations in Hybrid Recommender Systems, ACM Transactions on Interactive Intelligent Systems, 2019.PDF icon kouki-tiis19.pdf (2.01 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)
H. Kang, Sehgal, V., and Getoor, L., GeoDDupe: A Novel Interface for Interactive Entity Resolution in Geospatial Data, in International Conference on Information Visualization, 2007.PDF icon kangiv07.pdf (1.34 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)
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)
H. Haidarian-Shahri, Namata, G. Mark, Navlakha, S., Deshpande, A., and Roussopoulos, N., A Graph-based Approach to Vehicle Tracking in Traffic Camera Video Streams, in 4th International Workshop on Data Management for Sensor Networks, 2007.PDF icon dmsn07.pdf (576.07 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)
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)
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)
H. Sharara and Getoor, L., Group Detection, Encyclopedia of Machine Learning, 2010.
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)
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)
O. Udrea, Getoor, L., and Miller, R., HOMER: Ontology Alignment Visualization and Analysis. 2007.PDF icon getoor-homer07.pdf (125.38 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)
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)
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)
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)
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, 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)
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)
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)
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)
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, Identifying Graphs from Noisy Observational Data, University of Maryland - College Park, 2012.PDF icon namata-phdthesis.pdf (1.51 MB)
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)
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. 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)
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. Licamele and Getoor, L., Indirect two-sided relative ranking: a robust similarity measure for gene expression data, BMC Bioinformatics, 2010.
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)
M. Bilgic, Information Acquisition in Structured Domains, University of Maryland - College Park, 2010.PDF icon mbilgic-phdthesis.pdf (4.68 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)
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)
L. Getoor, An Introduction to Probabilistic Graphical Models for Relational Data, Data Engineering Bulletin, vol. 29, 2006.
L. Getoor and Taskar, B., Introduction to Statistical Relational Learning. The MIT Press, 2007.