Publications

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Author [ Title(Desc)] Year
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A
Bilgic, M. & Getoor, L. Active Inference for Collective Classification. Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track) 1652–1655 (2010). (387.53 KB)
Chen, D. et al. Active Inference for Retrieval in Camera Networks. Workshop on Person Oriented Vision (2011). (1.53 MB)
Sharara, H., Getoor, L. & Norton, M. An Active Learning Approach for Identifying Key Opinion Leaders. The 2nd Workshop on Information in Networks (WIN) (2010).
Bilgic, M., Mihalkova, L. & Getoor, L. Active Learning for Networked Data. Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010). (515.65 KB)
Sharara, H., Getoor, L. & Norton, M. Active Surveying. NIPS Workshop on Networks Across Disciplines in Theory and Applications (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). (349.39 KB)
Sharara, H., Norton, M. & Getoor, L. Active Surveying for Leadership Identification. The International Sunbelt Social Networks Conference XXX (2010).
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). (711.26 KB)
Embar, V., Farnadi, G., Pujara, J. & Getoor, L. Aligning Product Categories using Anchor Products. First Workshop on Knowledge Base Construction, Reasoning and Mining (2018). (577.65 KB)
B
desJardins, M., Rathod, P. & Getoor, L. Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence. 16th European Conference on Machine Learning (ECML) (2005).
Ramakrishnan, N. et al. ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2014). (1.15 MB)
London, B., Huang, B. & Getoor, L. The Benefits of Learning with Strongly Convex Approximate Inference. International Conference on Machine Learning (ICML) (2015). (788.06 KB)
Sen, P., Deshpande, A. & Getoor, L. Bisimulation-based Approximate Lifted Inference. Uncertainty in Artificial Intelligence (2009). (240.89 KB)
Pujara, J., London, B. & Getoor, L. Budgeted Online Collective Inference. Uncertainty in Artificial Intelligence (2015). (302.63 KB)
Pujara, J. & Getoor, L. Building Dynamic Knowledge Graphs. NIPS Workshop on Automated Knowledge Base Construction (2014). (143.26 KB)
C
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). (663.26 KB)
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). (421.14 KB)
Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Magazine 37, 63–75 (2016). (1.23 MB)
Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Chance-Constrained Programs for Link Prediction. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009). (161.38 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).
Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018). (299.32 KB)
Pujara, J. & Getoor, L. Coarse-to-Fine, Cost-Sensitive Classification of E-Mail. NIPS Workshop on Coarse-to-Fine Processing (2010). (258.86 KB)
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). (900 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).
London, B. et al. Collective Activity Detection using Hinge-loss Markov Random Fields. CVPR Workshop on Structured Prediction: Tractability, Learning and Inference (2013). (705.87 KB)
Sen, P., Namata, G. Mark, Bilgic, M. & Getoor, L. Collective Classification. Encyclopedia of Machine Learning (2010).
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. et al. Collective Classification in Network Data. AI Magazine 29, 93–106 (2008). (497.82 KB)
London, B. & Getoor, L. Data Classification: Algorithms and Applications (Aggarwal, C.) (CRC Press, 2013). (394.37 KB)
Bhattacharya, I. & Getoor, L. Collective Entity Resolution In Relational Data. ACM Transactions on Knowledge Discovery from Data 1, 1-36 (2007). (346.13 KB)
Bhattacharya, I. Collective Entity Resolution In Relational Data. (2006). (761.21 KB)
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> (653.4 KB)
Kouki, P., Pujra, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Multi-Relational Familial Networks. Knowledge and Information Systems (KAIS) (2018).
Bhattacharya, I. & Getoor, L. Collective Entity Resolution in Relational Data. Data Engineering Bulletin 29, (2006).
Namata, G. Mark, Kok, S. & Getoor, L. Collective Graph Identification. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011). (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). (500.96 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).
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> (463.69 KB)
Bhattacharya, I. & Getoor, L. CRC Data Mining Series 223-243 (Chapman and Hall, 2008).
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). (573.89 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). (373.82 KB)
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). (190.8 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).
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). (105.13 KB)
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).
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).
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> (624.33 KB)
Broecheler, M. & Getoor, L. Computing marginal distributions over continuous Markov networks for statistical relational learning. Advances in Neural Information Processing Systems (NIPS) (2010). (382.51 KB)
Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. Data Mining and Knowledge Discovery, Special Issue on Utility Based Data Mining 17, 136–163 (2008). (424.09 KB)
Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. SIAM Data Mining Workshop on Link Analysis, Counterterrorism and Security (2006). (137.37 KB)

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