Publications

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Broecheler, M. & Getoor, L. Computing marginal distributions over continuous Markov networks for statistical relational learning. Advances in Neural Information Processing Systems (NIPS) (2010).PDF icon broecheler-nips10.pdf (382.51 KB)
Augustine, E. & Getoor, L. A Comparison of Bottom-Up Approaches to Grounding for Templated Markov Random Fields. SysML (2018). at <https://github.com/eriq-augustine/grounding-experiments>PDF icon augustine-sysml18.pdf (624.33 KB)
Polymeropoulos, M. et al. Common effect of antipsychotics on the biosynthesis and regulation of fatty acids and cholesterol supports a key role of lipid homeostasis in schizophrenia. Schizophrenia Research (2009).
Udrea, O. & Getoor, L. Combining statistical and logical inference for ontology alignment. Workshop on Semantic Web for Collaborative Knowledge Acquisition at the International Joint Conference on Artificial Intelligence (2007).
Bilgic, M., Namata, G. Mark & Getoor, L. Combining Collective Classification and Link Prediction. Workshop on Mining Graphs and Complex Structures at the IEEE International Conference on Data Mining (ICDM-2007) (2007).PDF icon mgcs07.pdf (105.13 KB)
Sridhar, D., Foulds, J., Huang, B., Walker, M. & Getoor, L. Collective classification of stance and disagreement in online debate forums. Bay Area Machine Learning Symposium (BayLearn) (2014).
Sridhar, D., Getoor, L. & Walker, M. Collective Stance Classification of Posts in Online Debate Forums. ACL Joint Workshop on Social Dynamics and Personal Attributes in Social Media (2014).PDF icon sridhar-aclws14.pdf (190.8 KB)
London, B., Huang, B., Taskar, B. & Getoor, L. Collective Stability in Structured Prediction: Generalization from One Example. Proceedings of the 30th International Conference on Machine Learning (ICML-13) (2013).PDF icon london-icml13-long.pdf (373.82 KB)
Fakhraei, S., Foulds, J., Shashanka, M. & Getoor, L. Collective Spammer Detection in Evolving Multi-Relational Social Networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (ACM, 2015).PDF icon fakhraei-kdd2015.pdf (573.89 KB)
Bhattacharya, I. & Getoor, L. CRC Data Mining Series 223-243 (Chapman and Hall, 2008).
Kimmig, A., Memory, A., Miller, R. & Getoor, L. A Collective, Probabilistic Approach to Schema Mapping. International Conference on Data Engineering (ICDE) (2017). at <https://github.com/alexmemory/kimmig-icde17/wiki>PDF icon kimmig-icde17.pdf (463.69 KB)
Fakhraei, S., Huang, B. & Getoor, L. Collective Inference and Multi-Relational Learning for Drug–Target Interaction Prediction. NIPS Workshop on Machine Learning in Computational Biology (MLCB) (2013).
Namata, G. Mark, Kok, S. & Getoor, L. Collective Graph Identification. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011).PDF icon namata-kdd11.pdf (185.7 KB)
Namata, G. Mark, London, B. & Getoor, L. Collective Graph Identification. ACM Transactions on Knowledge Discovery from Data 10, 25:1–25:36 (2015).PDF icon namata-tkdd.pdf (500.96 KB)
Bhattacharya, I. & Getoor, L. Collective Entity Resolution in Relational Data. Data Engineering Bulletin 29, (2006).
Kouki, P., Pujra, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Multi-Relational Familial Networks. Knowledge and Information Systems (KAIS) (2018).PDF icon kouki-kais18.pdf (1.17 MB)
Kouki, P., Pujara, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Familial Networks. IEEE International Conference on Data Mining (ICDM) (2017). at <https://github.com/pkouki/icdm2017>PDF icon kouki-icdm17.pdf (653.4 KB)
Bhattacharya, I. & Getoor, L. Collective Entity Resolution In Relational Data. ACM Transactions on Knowledge Discovery from Data 1, 1-36 (2007).PDF icon bhattacharya-tkdd.pdf (346.13 KB)
London, B. & Getoor, L. Data Classification: Algorithms and Applications (Aggarwal, C.) (CRC Press, 2013).PDF icon cc-chapter.pdf (394.37 KB)
Sen, P. et al. Collective Classification in Network Data. AI Magazine 29, 93–106 (2008).PDF icon sen-aimag08.pdf (497.82 KB)
Namata, G. Mark, Sen, P., Bilgic, M. & Getoor, L. Text Mining: Classification, Clustering, and Applications (Sahami, M. & Srivastava, A.) (Taylor and Francis Group, 2009).
Sen, P., Namata, G. Mark, Bilgic, M. & Getoor, L. Collective Classification. Encyclopedia of Machine Learning (2010).
Embar, V., Pujara, J. & Getoor, L. Collective Alignment of Large-scale Ontologies. AKBC Workshop on Federated KBs and the Open Knowledge Network (2019).PDF icon embar-akbc19.pdf (57.36 KB)
London, B. et al. Collective Activity Detection using Hinge-loss Markov Random Fields. CVPR Workshop on Structured Prediction: Tractability, Learning and Inference (2013).PDF icon london-cvpr13.pdf (705.87 KB)
Lansky, A., Friedman, M., Getoor, L., Schmidler, S. & Jr., N. Short. The Collage/Khoros Link: Planning for Image Processing Tasks. Proceedings of the AAAI Spring Symposium on Integrated Planning Applications (1995).
Zheleva, E., Sharara, H. & Getoor, L. Co-evolution of Social and Affiliation Networks. 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2009).PDF icon fp659-zheleva.pdf (900 KB)
Pujara, J. & Getoor, L. Coarse-to-Fine, Cost-Sensitive Classification of E-Mail. NIPS Workshop on Coarse-to-Fine Processing (2010).PDF icon pujara_nips10.pdf (258.86 KB)
Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018).PDF icon chang-sysml18.pdf (299.32 KB)
Islamaj, R., Getoor, L. & W. Wilbur, J. Characterizing RNA secondary-structure features and their effects on splice-site prediction. IEEE ICDM Workshop on Mining and Management of Biological Data (2007).
Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Chance-Constrained Programs for Link Prediction. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).PDF icon doppa-nips09wkshp.pdf (161.38 KB)
Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Magazine 37, 63–75 (2016).PDF icon muthiah-aimag16.pdf (1.23 MB)
Licamele, L., Bilgic, M., Getoor, L. & Roussopoulos, N. Capital and Benefit in Social Networks. ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD) (2005).PDF icon licamele_linkkdd05.pdf (421.14 KB)
Kang, H., Getoor, L. & Singh, L. C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership. Visual Analytics Science and Technology (VAST) (2007).PDF icon vast07-kang.pdf (663.26 KB)
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Embar, V., Farnadi, G., Pujara, J. & Getoor, L. Aligning Product Categories using Anchor Products. First Workshop on Knowledge Base Construction, Reasoning and Mining (2018).PDF icon embar-kbcom18.pdf (577.65 KB)
Fakhraei, S., Sridhar, D., Pujara, J. & Getoor, L. Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. 12th International SIGKDD Workshop on Mining and Learning with Graphs (MLG) (ACM SIGKDD, 2016).PDF icon fakhraei_mlg_2016.pdf (711.26 KB)
Sharara, H., Norton, M. & Getoor, L. Active Surveying for Leadership Identification. The International Sunbelt Social Networks Conference XXX (2010).
Sharara, H., Getoor, L. & Norton, M. Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders. The 22nd International Joint Conference on Artificial Intelligence (IJCAI '11) (2011).PDF icon sharara_paper736.pdf (349.39 KB)
Sharara, H., Getoor, L. & Norton, M. Active Surveying. NIPS Workshop on Networks Across Disciplines in Theory and Applications (2010).
Bilgic, M., Mihalkova, L. & Getoor, L. Active Learning for Networked Data. Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010).PDF icon bilgic-icml10.pdf (515.65 KB)
Sharara, H., Getoor, L. & Norton, M. An Active Learning Approach for Identifying Key Opinion Leaders. The 2nd Workshop on Information in Networks (WIN) (2010).
Chen, D. et al. Active Inference for Retrieval in Camera Networks. Workshop on Person Oriented Vision (2011).PDF icon chen-wpov11.pdf (1.53 MB)
Bilgic, M. & Getoor, L. Active Inference for Collective Classification. Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track) 1652–1655 (2010).PDF icon bilgic-aaai10.pdf (387.53 KB)
Kimmig, A., Memory, A., Miller, R. J. & Getoor, L. A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence. IEEE Transactions on Knowledge and Data Engineering 31, 1426-1439 (2019).PDF icon kimming-tkde19.pdf (713.64 KB)

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