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U. Chajewska, Getoor, L., and Norman, J., Utility Elicitation as a Classification Problem, in Proceedings of the AAAI Spring Symposium Series on Interactive and Mixed Initiative Decision-Theoretic Systems, 1998.
J. Chang, Chen, R., Pujara, J., and Getoor, L., Clustering System Data using Aggregate Measures, in Machine Learning and Systems (MLSys), 2018.PDF icon chang-sysml18.pdf (299.32 KB)
D. Chen, Bilgic, M., Getoor, L., and Jacobs, D., Efficient Resource-constrained Retrospective Analysis of Long Video Sequences, in NIPS Workshop on Adaptive Sensing, Active Learning and Experimental Design: Theory, Methods and Applications, 2009.PDF icon chen-nips09-wkshp.pdf (383.38 KB)
C. Daozheng, Mustafa, B., Getoor, L., David, J., Lilyana, M., and Tom, Y., Active Inference for Retrieval in Camera Networks, in IEEE Workshop on Person-Oriented Vision, 2011.PDF icon chen-wpov11.pdf (1.53 MB)
C. Daozheng, Mustafa, B., Getoor, L., and David, J., Dynamic Processing Allocation in Video, PAMI, vol. 33, pp. 2174-2187, 2011.PDF icon chen-pami11.pdf (1.16 MB)
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)
C. Diehl, Namata, G. Mark, and Getoor, L., Relationship Identification for Social Network Discovery, in AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence, 2007.PDF icon diehl-aaai07.pdf (139.6 KB)
C. Diehl, Getoor, L., and Namata, G. Mark, Name Reference Resolution in Organizational Email Archives, in SIAM Conference on Data Mining (SDM), 2006.PDF icon diehlsdm06.pdf (971.2 KB)
T. Dietterich, Domingos, P., Getoor, L., Muggleton, S., and Tadepalli, P., Structured machine learning: the next ten years, Machine Learning, vol. 73, pp. 3–23, 2008.
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. Doppa, Yu, J., Tadepalli, P., and Getoor, L., Chance-Constrained Programs for Link Prediction, in NIPS Workshop on Analyzing Networks and Learning with Graphs, 2009.PDF icon doppa-nips09wkshp.pdf (161.38 KB)
T. Elsayed, Oard, D., Namata, G. Mark, and Getoor, L., Personal Name Resolution in Email: A Heuristic Approach. University of Maryland, College Park, 2008.PDF icon LAMP_150.pdf (397.61 KB)
T. Elsayed, Oard, D., and Namata, G. Mark, Resolving Personal Names in Email Using Context Expansion, in 46th Annual Meeting of the Association of Computational Linguistics, 2008, pp. 265–268.PDF icon elsayed-acl08.pdf (294.84 KB)
V. Embar, Farnadi, G., Pujara, J., and Getoor, L., Aligning Product Categories using Anchor Products, in Workshop on Knowledge Base Construction, Reasoning and Mining (KBCOM), 2018.PDF icon embar-kbcom18.pdf (577.65 KB)
V. Embar, Sridhar, D., Farnadi, G., and Getoor, L., Scalable Structure Learning for Probabilistic Soft Logic, in IJCAI Workshop on Statistical Relational AI (StarAI), 2018.PDF icon embar-starai18.pdf (400.23 KB)
V. Embar, Pujara, J., and Getoor, L., Collective Alignment of Large-scale Ontologies, in AKBC Workshop on Federated Knowledge Bases (FKBs), 2019.PDF icon embar-fkbs19.pdf (57.36 KB)
V. Embar, Srinivasan, S., and Getoor, L., Tractable Marginal Inference for Hinge-Loss Markov Random Fields, in ICML Workshop on Tractable Probabilistic Modeling (TPM), 2019.PDF icon embar-tpm19.pdf (410.24 KB)
V. Embar, Srinivasan, S., and Getoor, L., Estimating Aggregate Properties In Relational Networks With Unobserved Data, in AAAI Workshop on Statistical Relational Artificial Intelligence (StarAI), 2020.PDF icon embar-starai20.pdf (257.9 KB)
V. Embar, Sisman, B., Wei, H., Dong, X. Luna, Faloutsos, C., and Getoor, L., Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage, in Automated Knowledge Base Construction (AKBC), 2020.PDF icon embar-akbc20.pdf (499.49 KB)
S. Fakhraei, Huang, B., Raschid, L., and Getoor, L., Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014.PDF icon fakhraei-tcbb2014_accepted.pdf (3.97 MB)
S. Fakhraei, Huang, B., and Getoor, L., Collective Inference and Multi-Relational Learning for Drug–Target Interaction Prediction, in NIPS Workshop on MLCB, 2013.PDF icon fakhraei-mlcb13.pdf (243.86 KB)
S. Fakhraei, Raschid, L., and Getoor, L., Drug-Target Interaction Prediction for Drug Repurposing with Probabilistic Similarity Logic, in KDD Workshop on BIOKDD, 2013.PDF icon fakhraei-biokdd13.pdf (669.27 KB)
S. Fakhraei, Foulds, J., Shashanka, M., and Getoor, L., Collective Spammer Detection in Evolving Multi-Relational Social Networks, in KDD, 2015.PDF icon fakhraei-kdd2015.pdf (573.89 KB)
S. Fakhraei, Onukwugha, E., and Getoor, L., Data Analytics for Pharmaceutical Discoveries, 1st ed., vol. 1. CRC Press, 2015, p. 1--25.PDF icon fakhraei-book15.pdf (234.2 KB)
S. Fakhraei, Dhanya, S., Pujara, J., and Getoor, L., Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks, in KDD, 2016.
G. Farnadi, Bach, S. H., Moens, M. - F., Getoor, L., and De Cock, M., Extending PSL with Fuzzy Quantifiers, in International Workshop on Statistical Relational Artificial Intelligence (StaRAI), 2014.PDF icon farnadi-starai14.pdf (196.15 KB)
G. Farnadi, Bach, S. H., Moens, M. - F., Getoor, L., and De Cock, M., Soft quantification in statistical relational learning, Machine Learning Journal, 2017.PDF icon farnadi-mlj17.pdf (1.24 MB)
G. Farnadi, Bach, S. H., Blondeel, M., Moens, M. - F., Getoor, L., and De Cock, M., Statistical Relational Learning with Soft Quantifiers, in International Conference on Inductive Logic Programming (ILP), 2015.PDF icon farnadi-ilp15.pdf (578.43 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)
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, Kouki, P., Thompson, S. K., Srinivasan, S., and Getoor, L., A Fairness-aware Hybrid Recommender System, in RecSys Workshop on Responsible Recommendation (FATREC), 2018.PDF icon farnadi-fatrec18.pdf (474.04 KB)
G. Farnadi, Babaki, B., and Getoor, L., A Declarative Approach to Fairness in Relational Domains, IEEE Data Engineering Bulletin, vol. 42, no. 3, p. 36--48, 2019.PDF icon farnadi-de19.pdf (365.14 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)
N. Friedman and Getoor, L., Efficient Learning Using Constrained Sufficient Statistics, in Uncertainty99, 1999.
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 and Machanavajjhala, A., Entity Resolution in Big Data, in KDD, 2013.PDF icon getoor-kdd13.pdf (7.16 MB)
L. Getoor and Machanavajjhala, A., Entity Resolution for Social Network Analysis and Mining, in IEEE ACM International Conference on Advances in Social Networks Analysis and Mining, 2012.
L. Getoor and Machanavajjhala, A., Entity Resolution: Theory, Practice & Open Challenges, in International Conference on Very Large Data Bases, 2012.PDF icon p2018_lisegetoor_vldb2012.pdf (89.45 KB)
L. Getoor and Machanavajjhala, A., Entity Resolution: Theory, Practice, and Open Challenges, in AAAI Conference on Artificial Intelligence, 2012.
L. Getoor and Mihalkova, L., Exploiting Statistical and Relational Information on the Web and in Social Media. 2011.PDF icon getoor-sdm11.pdf (88.88 KB)
L. Getoor and Scheffer, T., Proceedings of the 28th International Conference on Machine Learning, in Proceedings of the 28th International Conference on Machine Learning, 2011.
L. Getoor and Taskar, B., Introduction to Statistical Relational Learning. The MIT Press, 2007.
L. Getoor, Friedman, N., Koller, D., Pfeffer, A., and Taskar, B., Probabilistic Relational Models, 1st ed., vol. 1. MIT Press, 2007, p. 129--174.PDF icon getoor-book07.pdf (648.15 KB)
L. Getoor, An Introduction to Probabilistic Graphical Models for Relational Data, Data Engineering Bulletin, vol. 29, 2006.
L. Getoor and Grant, J., PRL: A Logical Approach to Probabilistic Relational Models, Machine Learning Journal, vol. 62, 2006.PDF icon getoor-mlj06.pdf (685.04 KB)
L. Getoor and Diehl, C., Link Mining: A Survey, SigKDD Explorations Special Issue on Link Mining, vol. 7, 2005.
L. Getoor, Link-based Classification, 1st ed., vol. 1. Springer-Verlag, 2005, p. 189--207.PDF icon getoor-book05.pdf (273.43 KB)
L. Getoor, Rhee, J., Koller, D., and Small, P., Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models, AI in Medicine Journal, vol. 30, pp. 233-256, 2004.
L. Getoor, Link Mining: A New Data Mining Challenge, SIGKDD Explorations, volume, vol. 5, p. 85- -89, 2003.
L. Getoor, Structure Discovery Using Statistical Relational Learning, Data Engineering Bulletin, vol. 26, p. 11- -18, 2003.