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

Export 320 results:
Author [ Title(Asc)] Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
P
E. Zheleva, Prediction, Evolution and Privacy in Social and Affiliation Networks, University of Maryland College Park, 2011.PDF icon zheleva-phdthesis11.pdf (5.81 MB)
L. Licamele and Getoor, L., Predicting Protein-Protein Interactions Using Relational Features, in ICML Workshop on Statistical Network Analysis, 2006.
S. Tomkins, Ramesh, A., and Getoor, L., Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study, in EDM, 2016.PDF icon tomkins-edm16.pdf (619.77 KB)
M. Rastegari, Choi, J., Fakhraei, S., III, H. Daume, and Davis, L., Predictable Dual-View Hashing, in Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013, pp. 1328–1336.PDF icon rastegari13.pdf (2.35 MB)
A. Lansky and Getoor, L., Practical Planning in COLLAGE, in Proceedings of the AAAI Fall Symposium on Planning and Learning: On to Real Applications, 1994.
P. Sen, Deshpande, A., and Getoor, L., PrDB: Managing and Exploiting Rich Correlations in Probabilistic Databases, VLDB Journal, special issue on uncertain and probabilistic databases, 2009.PDF icon sen-vldbj09.pdf (1.12 MB)
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)
P. Kouki, Schaffer, J., Pujara, J., Odonovan, J., and Getoor, L., Personalized Explanations for Hybrid Recommender Systems, in Intelligent User Interfaces (IUI), 2019.PDF icon kouki-iui19.pdf (3.34 MB)
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)
S. H. Bach, Huang, B., Boyd-Graber, J., and Getoor, L., Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs, in International Conference on Machine Learning (ICML), 2015.PDF icon bach-icml15.pdf (356.46 KB)
E. Hung, Getoor, L., and Subrahmanian, V. S., PXML: A Probabilistic Semistructured Data Model and Algebra, in Proceedings of the IEEE International Conference on Data Engineering, 2003.
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)
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, 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)
O
H. Hwang, Lauw, H., Getoor, L., and Ntoulas, A., Organizing User Search Histories, IEEE Transactions on Knowledge and Data Engineering, 2010.
S. Somasundaran, Namata, G. Mark, Getoor, L., and Wiebe, J., Opinion Graphs for Polarity and Discourse Classification, in TextGraphs-4: Graph-based Methods for Natural Language Processing, 2009.PDF icon somasundaran-textgraphs09.pdf (289.75 KB)
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)
L. Getoor and Fromherz, M., Online Scheduling for Reprographic Machines, in Working notes AAAI Workshop on Online Search, 1997.
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)
I. Bhattacharya and Getoor, L., Online Collective Entity Resolution, in The 22nd National Conference on Artificial Intelligence (NECTAR Track), 2007.PDF icon nectar07.pdf (395.24 KB)
M
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)
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)
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)
L. Getoor, Multi-relational Data Mining Using Probabilistic Models, in Multi-Relational Data Mining Workshop, 2001.PDF icon mrdm.pdf (109.57 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.
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)
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)
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)
L
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)
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)
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)
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)
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)
G. Mark Namata and Getoor, L., Link Prediction, Encyclopedia of Machine Learning, 2010.
L. Getoor and Diehl, C., Link Mining: A Survey, SigKDD Explorations Special Issue on Link Mining, vol. 7, 2005.
L. Getoor, Link Mining: A New Data Mining Challenge, SIGKDD Explorations, volume, vol. 5, p. 85- -89, 2003.
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)
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)
L. Mihalkova and Getoor, L., Lifted Graphical Models: A Survey. 2011.PDF icon 1107.4966v2.pdf (446.54 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.
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)
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. Getoor, Friedman, N., and Koller, D., Learning Structured Statistical Models from Relational Data, Electronic Transactions on Artificial Intelligence, vol. 6, 2002.
L. Getoor, Learning Statistical Models from Relational Data, Stanford, 2001.PDF icon getoor-thesis.pdf (3.39 MB)
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.

Pages