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K

P. Kouki, Pujara, J., Marcum, C., Koehly, L., and Getoor, L., “Collective Entity Resolution in Multi-Relational Familial Networks”, Knowledge and Information Systems (KAIS), vol. 61, no. 3, p. 1547-–1581, 2018.

kouki-kais18.pdf (1.17 MB)

P. Kouki, “Resolution, Recommendation, and Explanation in Richly Structured Social Networks”, 2018.

kouki-dissertation.pdf (6.94 MB)

P. Kouki, Schaffer, J., Pujara, J., Odonovan, J., and Getoor, L., “Personalized Explanations for Hybrid Recommender Systems”, in Intelligent User Interfaces (IUI), 2019.

kouki-iui19.pdf (3.34 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.

kouki-tiis19.pdf (2.01 MB)

S. Kumar, Pujara, J., Getoor, L., Mares, D., Gupta, D., and Riloff, E., “Unsupervised Models for Predicting Strategic Relations between Organizations”, in ASONAM, 2016.

kumar-asonam16.pdf (212.61 KB)

L

A. Lansky and Getoor, L., “Scope and Abstraction: Two Criteria for Localized Planning”, in Proceedings of the International Joint Conference on Articial Intelligence, 1995.

A. Lansky, Friedman, M., Getoor, L., Schmidler, S., and Jr., N. Short, “The Collage/Khoros Link: Planning for Image Processing Tasks”, in Proceedings of the AAAI Spring Symposium on Integrated Planning Applications, 1995.

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.

A. Lansky and Getoor, L., “Scope and Abstraction: Two Criteria for Localized Planning”, in Proceedings of the Workshop on Theory Reformulation and Abstraction, 1994.

K. Lerman, Getoor, L., Minton, S., and Knoblock, C., “Using the Structure of Web Sites for Automatic Segmentation of Tables”, in In Proceedings of ACM-SIGMOD 2004 International Conference on Management of Data, 2004.

lerman-sigmod04.pdf (307.43 KB)

L. Licamele and Getoor, L., “A method for the detection of meaningful and reproducible group signatures from gene expression profiles.”, Journal of Bioinformatics and Computational Biology, 2011.

L. Licamele and Getoor, L., “Indirect two-sided relative ranking: a robust similarity measure for gene expression data”, BMC Bioinformatics, 2010.

L. Licamele and Getoor, L., “Predicting Protein-Protein Interactions Using Relational Features”, in ICML Workshop on Statistical Network Analysis, 2006.

L. Licamele and Getoor, L., “Social Capital in Friendship-Event Networks”, in IEEE International Conference on Data Mining (ICDM), 2006.

L. Licamele, Bilgic, M., Getoor, L., and Roussopoulos, N., “Capital and Benefit in Social Networks”, in ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD), 2005.

licamele_linkkdd05.pdf (421.14 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.

london-aistats14.pdf (490.14 KB)

B. London, Khamis, S., Bach, S., Huang, B., Getoor, L., and Davis, L., “Collective Activity Detection using Hinge-loss Markov Random Fields”, in CVPR Workshop on SPTLE, 2013.

london-sptle13.pdf (705.87 KB)

B. London and Getoor, L., Collective Classification of Network Data, 1st ed., vol. 1. CRC Press, 2013, p. 399--416.

london-book13.pdf (394.37 KB)

B. London, Huang, B., Taskar, B., and Getoor, L., “Collective Stability in Structured Prediction: Generalization from One Example”, in ICML, 2013.

london-icml13.pdf (373.82 KB)

B. London, Huang, B., and Getoor, L., “Graph-based Generalization Bounds for Learning Binary Relations”. University of Maryland College Park, 2013.

br_risk_bounds.pdf (304.54 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.

mrwtd.pdf (460.45 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.

london-nips13ws.pdf (205.57 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.

london-nips12ws.pdf (213.95 KB)

B. London, Rekatsinas, T., Huang, B., and Getoor, L., “Multi-relational Weighted Tensor Decomposition”, in NIPS Workshop on SL, 2012.

london-sl12.pdf (326.3 KB)

B. London, “On the Stability of Structured Prediction”, University of Maryland, 2015.

blondon-thesis.pdf (1.16 MB)

B. London, Huang, B., and Getoor, L., “Stability and Generalization in Structured Prediction”, –, 2015.

london-stability15.pdf (532.16 KB)

B. London, Huang, B., and Getoor, L., “The Benefits of Learning with Strongly Convex Approximate Inference”, in ICML, 2015.

london-icml15.pdf (788.06 KB)

B. London, Huang, B., and Getoor, L., “Stability and Generalization in Structured Prediction”, Journal of Machine Learning Research, vol. 17, 2016.

london-jmlr17.pdf (532.8 KB)

B. London, Huang, B., and Getoor, L., “On the Strong Convexity of Variational Inference”, in NIPS Workshop on Advances in Variational Inference, 2014.

london-nips14ws.pdf (253.72 KB)

Q. Lu and Getoor, L., “Link-based Classification”, in Proceedings of the International Conference on Machine Learning (ICML), 2003.

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.

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.

ijcai03-ws.pdf (97.25 KB)

M

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.

mrc_iswc12_ws.pdf (411.71 KB)

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.

miao-bd13.pdf (307.51 KB)

L. Mihalkova, Moustafa, W. Eldin, and Getoor, L., “Learning to Predict Web Collaborations”, in WSDM Workshop on User Modeling for Web Applications, 2011.

mihalkova-wikiCollabs.pdf (353.9 KB)

L. Mihalkova and Getoor, L., “Lifted Graphical Models: A Survey”. 2011.

1107.4966v2.pdf (446.54 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.

minton-icmla2011.pdf (733.09 KB)

W. Eldin Moustafa, Kimmig, A., Deshpande, A., and Getoor, L., “Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty”, in International Conference on Data Engineering (ICDE), 2014.

ICDE14_conf_full_374.pdf (1.57 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.

moustafa-sigmod13.pdf (1.1 MB)

W. Eldin Moustafa, Deshpande, A., and Getoor, L., “Ego-centric Graph Pattern Census”, in International Conference on Data Engineering (ICDE), 2012.

moustafaicde.pdf (2.27 MB)

W. Moustafa, Namata, G., Deshpande, A., and Getoor, L., “Declarative Analysis of Noisy Information Networks”, in ICDE Workshop on GDM, 2011.

moustafa-gdm11.pdf (1.55 MB)

S. Muthiah, Huang, B., Arredondo, J., Mares, D., Getoor, L., Katz, G., and Ramakrishnan, N., “Capturing Planned Protests from Open Source Indicators”, AI Mag, vol. 37, pp. 63–75, 2016.

muthiah-aimag16.pdf (1.23 MB)

N

G. Mark Namata, “Identifying Graphs from Noisy Observational Data”, University of Maryland - College Park, 2012.

namata-phdthesis.pdf (1.51 MB)

G. Namata, London, B., Getoor, L., and Huang, B., “Query-driven Active Surveying for Collective Classification”, in ICML Workshop on MLG, 2012.

namata-mlg12.pdf (257.49 KB)

G. Namata, Kok, S., and Getoor, L., “Collective Graph Identification”, in KDD, 2011.

namata-kdd11.pdf (185.7 KB)

G. Namata, Sharara, H., and Getoor, L., A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks, 1st ed., vol. 1. Springer, 2010, p. 107--133.

namata-book10.pdf (656.83 KB)

G. Mark Namata and Getoor, L., “Link Prediction”, Encyclopedia of Machine Learning, 2010.

G. Mark Namata and Getoor, L., “A Pipeline Approach to Graph Identification”, in Seventh International Workshop on Mining and Learning with Graphs, 2009.

namatag-mlg09.pdf (93.77 KB)

G. Namata, Sen, P., Bilgic, M., and Getoor, L., Collective Classification for Text Classification, 1st ed., vol. 1. Taylor and Francis Group, 2009, p. 51--69.

namata-book09.pdf (4.35 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.

namatag-kddu09.pdf (241.7 KB)

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