Publications

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Pujara, J. & Skomoroch, P. Large-Scale Hierarchical Topic Models. NIPS Workshop on BigLearn (2012).PDF icon pujara_biglearn12.pdf (189.96 KB)
Pujara, J., London, B. & Getoor, L. Reducing Label Cost by Combining Feature Labels and Crowdsourcing. ICML Workshop on Combining Learning Strategies to Reduce Label Cost (2011).PDF icon clsicml_pujara_london.pdf (253.29 KB)
Pujara, J., III, H. Daume & Getoor, L. Using Classifier Cascades for Scalable E-Mail Classification. Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (ACM, 2011).PDF icon pujara_ceas2011_camera.pdf (308.42 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)
Pujara, J., Augustine, E. & Getoor, L. Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short. Conference on Empirical Methods in Natural Language Processing (EMNLP) (2017). at <https://github.com/eriq-augustine/meta-kg>PDF icon pujara-emnlp17.pdf (677.74 KB)
Pujara, J., London, B. & Getoor, L. Budgeted Online Collective Inference. UAI (2015).PDF icon pujara-uai15.pdf (302.63 KB)
Pujara, J., London, B., Getoor, L. & Cohen, W. Online Inference for Knowledge Graph Construction. Workshop on Statistical Relational AI (2015).PDF icon pujara-starai15.pdf (340.95 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Using Semantics & Statistics to Turn Data into Knowledge. AI Magazine 36, 65–74 (2015).PDF icon pujara_aimag15.pdf (359.48 KB)
Pujara, J. Probabilistic Models for Scalable Knowledge Graph Construction. (2016).PDF icon pujara-thesis15.pdf (1.06 MB)
Pujara, J. & Getoor, L. Generic Statistical Relational Entity Resolution in Knowledge Graphs. StarAI (IJCAI 2016, 2016). doi:2016PDF icon pujara-starai16.pdf (151.37 KB)
Pujara, J. & Getoor, L. Building Dynamic Knowledge Graphs. NIPS Workshop on Automated Knowledge Base Construction (2014).PDF icon pujara_akbc14.pdf (143.26 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).
Plangprasopchok, A., Lerman, K. & Getoor, L. A Probabilistic Approach for Learning Folksonomies from Structured Data. Fourth ACM International Conference on Web Search and Data Mining (WSDM) (2011).
Plangprasopchok, A., Lerman, K. & Getoor, L. Growing a tree in the forest: constructing folksonomies by integrating structured metadata. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010).PDF icon plang-kdd10.pdf (705.71 KB)
Piatetsky-Shapiro, G. et al. Is there a grand challenge or X-prize for data mining?. 12th International Conference on Knowledge Discovery and Data Mining (2006).
Panagiotis, P., Panayiotis, T., Ariel, F. & Getoor, L. TACI: Taxonomy-Aware Catalog Integration. TKDE 25, (2012).PDF icon papadimitriou-tkde12.pdf (2.93 MB)
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Namata, G. Mark. Identifying Graphs from Noisy Observational Data. (2012).PDF icon namata-phdthesis.pdf (1.51 MB)
Namata, G., London, B., Getoor, L. & Huang, B. Query-driven Active Surveying for Collective Classification. ICML Workshop on MLG (2012).PDF icon namata-mlg12.pdf (257.49 KB)
Namata, G., Kok, S. & Getoor, L. Collective Graph Identification. KDD (2011).PDF icon namata-kdd11.pdf (185.7 KB)
Namata, G., Sharara, H. & Getoor, L. A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks. Link Mining: Models, Algorithms, and Applications 1, 107--133 (Springer, 2010).PDF icon namata-book10.pdf (656.83 KB)
Namata, G. Mark & Getoor, L. Link Prediction. Encyclopedia of Machine Learning (2010).
Namata, G. Mark & Getoor, L. A Pipeline Approach to Graph Identification. Seventh International Workshop on Mining and Learning with Graphs (2009).PDF icon namatag-mlg09.pdf (93.77 KB)
Namata, G., Sen, P., Bilgic, M. & Getoor, L. Collective Classification for Text Classification. Text Mining: Classification, Clustering, and Applications 1, 51--69 (Taylor and Francis Group, 2009).PDF icon namata-book09.pdf (4.35 MB)
Namata, G. Mark & Getoor, L. Identifying Graphs From Noisy and Incomplete Data. 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (2009).PDF icon namatag-kddu09.pdf (241.7 KB)
Namata, G. Mark, Staats, B., Getoor, L. & Shneiderman, B. A Dual-View Approach to Interactive Network Visualization. ACM Conference on Information and Knowledge Management (2007).PDF icon cikm0671-namata.pdf (376.54 KB)
Namata, G. Mark, Getoor, L. & Diehl, C. Inferring Organizational Titles in Online Communications. ICML Workshop on Statistical Network Analysis (2006).PDF icon icml2006_ExtAbst.pdf (72.39 KB)
Namata, G., London, B. & Getoor, L. Collective Graph Identification. TKDD 10, (2015).PDF icon namata-tkdd15.pdf (500.96 KB)
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Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Mag 37, 63–75 (2016).PDF icon muthiah-aimag16.pdf (1.23 MB)
Moustafa, W. Eldin, Kimmig, A., Deshpande, A. & Getoor, L. Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty. International Conference on Data Engineering (ICDE) (2014).PDF icon ICDE14_conf_full_374.pdf (1.57 MB)
Moustafa, W., Miao, H., Deshpande, A. & Getoor, L. GrDB: A System for Declarative and Interactive Analysis of Noisy Information Networks. SIGMOD (2013).PDF icon moustafa-sigmod13.pdf (1.1 MB)
Moustafa, W. Eldin, Deshpande, A. & Getoor, L. Ego-centric Graph Pattern Census. International Conference on Data Engineering (ICDE) (2012).PDF icon moustafaicde.pdf (2.27 MB)
Moustafa, W., Namata, G., Deshpande, A. & Getoor, L. Declarative Analysis of Noisy Information Networks. ICDE Workshop on GDM (2011).PDF icon moustafa-gdm11.pdf (1.55 MB)
Minton, S. et al. Improving Classifier Performance by Autonomously Collecting Background Knowledge from the Web. Tenth International Conference on Machine Learning and Applications (2011).PDF icon minton-icmla2011.pdf (733.09 KB)
Mihalkova, L., Moustafa, W. Eldin & Getoor, L. Learning to Predict Web Collaborations. WSDM Workshop on User Modeling for Web Applications (2011).PDF icon mihalkova-wikiCollabs.pdf (353.9 KB)
Mihalkova, L. & Getoor, L. Lifted Graphical Models: A Survey. (2011).PDF icon 1107.4966v2.pdf (446.54 KB)
Miao, H., Liu, X., Huang, B. & Getoor, L. A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization. 2013 IEEE International Conference on Big Data (2013).PDF icon miao-bd13.pdf (307.51 KB)
Memory, A., Kimmig, A., Bach, S. H., Raschid, L. & Getoor, L. Graph Summarization in Annotated Data Using Probabilistic Soft Logic. Proceedings of the International Workshop on Uncertainty Reasoning for the Semantic Web (URSW) (2012).PDF icon mrc_iswc12_ws.pdf (411.71 KB)
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Lu, Q. & Getoor, L. Link-based Classification. Proceedings of the International Conference on Machine Learning (ICML) (2003).PDF icon lu-icml03.pdf (195.81 KB)
Lu, Q. & Getoor, L. Link-based Classification Using Labeled and Unlabeled Data. 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)
Lu, Q. & Getoor, L. Link-based Text Classification. IJCAI Workshop on "Text Mining and Link Analysis" (2003).PDF icon ijcai03-ws.pdf (97.25 KB)
London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayesian Collective Stability. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (2014).PDF icon london-aistats14.pdf (490.14 KB)
London, B. et al. Collective Activity Detection using Hinge-loss Markov Random Fields. CVPR Workshop on SPTLE (2013).PDF icon london-sptle13.pdf (705.87 KB)
London, B. & Getoor, L. Collective Classification of Network Data. Data Classification: Algorithms and Applications 1, 399--416 (CRC Press, 2013).PDF icon london-book13.pdf (394.37 KB)
London, B., Huang, B., Taskar, B. & Getoor, L. Collective Stability in Structured Prediction: Generalization from One Example. ICML (2013).PDF icon london-icml13.pdf (373.82 KB)
London, B., Huang, B. & Getoor, L. Graph-based Generalization Bounds for Learning Binary Relations. (2013).PDF icon br_risk_bounds.pdf (304.54 KB)
London, B., Rekatsinas, T., Huang, B. & Getoor, L. Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss. (2013).PDF icon mrwtd.pdf (460.45 KB)
London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayes Generalization Bounds for Randomized Structured Prediction. NIP Workshop on Perturbation, Optimization and Statistics (2013).PDF icon london-nips13ws.pdf (205.57 KB)
London, B., Huang, B. & Getoor, L. Improved Generalization Bounds for Large-scale Structured Prediction. NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Networks (2012).PDF icon london-nips12ws.pdf (213.95 KB)
London, B., Rekatsinas, T., Huang, B. & Getoor, L. Multi-relational Weighted Tensor Decomposition. NIPS Workshop on SL (2012).PDF icon london-sl12.pdf (326.3 KB)
London, B. On the Stability of Structured Prediction. (2015).PDF icon blondon-thesis.pdf (1.16 MB)

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