Publications

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Farnadi, G. et al. Statistical Relational Learning with Soft Quantifiers. International Conference on Inductive Logic Programming (ILP) (2015).PDF icon farnadi-ilp15.pdf (578.43 KB)
London, B., Huang, B. & Getoor, L. On the Strong Convexity of Variational Inference. NIPS Workshop on Advances in Variational Inference (2014).PDF icon london-nips14ws.pdf (253.72 KB)
Getoor, L. Structure Discovery Using Statistical Relational Learning. Data Engineering Bulletin 26, 11- -18 (2003).
Zhang, Y., Ramesh, A., Golbeck, J., Sridhar, D. & Getoor, L. A Structured Approach to Understanding Recovery and Relapse in AA. The Web Conference (WWW) (2018). at <https://github.com/yzhan202/zhang-www18-experiments>PDF icon zhang-www18.pdf (800.66 KB)
Dietterich, T., Domingos, P., Getoor, L., Muggleton, S. & Tadepalli, P. Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008).
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)
Somasundaran, S., Namata, G. Mark, Wiebe, J. & Getoor, L. Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification. Conference on Empirical Methods in Natural Language Processing (2009).PDF icon somasundaran-emnlp09.pdf (390.93 KB)
Namata, G. Mark, Sharara, H. & Getoor, L. Link Mining: Models, Algorithms, and Applications (Yu, J. Han Philip & Faloutsos, C.) (Springer, 2010).
Tomkins, S., Isley, S., London, B. & Getoor, L. Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations. Recommender Systems (RecSys) (2018).PDF icon recsys_2018.pdf (655.92 KB)
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Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs. ACM Conference on Learning at Scale (ACM, 2014).
Sharara, H., Singh, L., Getoor, L. & Mann, J. Understanding Actor Loyalty to Event-Based Groups in Affiliation Networks. Journal of Advances in Social Networks Analysis and Mining 1, 115–126 (2011).
Ramesh, A. & Getoor, L. Understanding Evolution of Long-running MOOCs. International Conference on Web Information Systems Engineering (WISE) (2018).
Ramesh, A., Rodriguez, M. & Getoor, L. Understanding Influence in Online Professional Networks. NIPS Workshop on Networks in Social and Information Sciences (2015).PDF icon ramesh-nipsws15.pdf (211.44 KB)
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Understanding MOOC Discussion Forums using Seeded LDA. 9th ACL Workshop on Innovative Use of NLP for Building Educational Applications (ACL, 2014).PDF icon ramesh-aclws14.pdf (137.57 KB)
Getoor, L., Rhee, J., Koller, D. & Small, P. Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models. AI in Medicine Journal 30, 233-256 (2004).
Grycner, A., Weikum, G., Pujara, J., Foulds, J. & Getoor, L. A Unified Probabilistic Approach for Semantic Clustering of Relational Phrases. NIPS Workshop on Automated Knowledge Base Construction (2014).
Bach, S. H., Huang, B. & Getoor, L. Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. Artificial Intelligence and Statistics (AISTATS) (2015).PDF icon bach-aistats15.pdf (345.2 KB)
Kumar, S. et al. Unsupervised Models for Predicting Strategic Relations between Organizations. IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE, 2016).PDF icon kumar_asonam16.pdf (212.61 KB)
Bhattacharya, I., Getoor, L. & Bengio, Y. Unsupervised Sense Disambiguation using Bilingual Probabilistic Models. Annual Meeting of the Association for Computational Linguistics (ACL) (2004).PDF icon acl04.pdf (156.26 KB)
Kouki, P., Schaffer, J., Pujara, J., ODonovan, J. & Getoor, L. User Preferences for Hybrid Explanations. 11th ACM Conference on Recommender Systems (RecSys) (2017).PDF icon kouki-recsys17.pdf (2.64 MB)
Ramesh, A., Yoo, J., Shen, S., Getoor, L. & Kim, J. User Role Prediction in Online Discussion Forums using Probabilistic Soft Logic. (2012).PDF icon Arti_nips_2012_final_version_1.pdf (759.57 KB)
Chajewska, U., Norman, J. & Getoor, L. Using Classi cation Techniques for Utility Elicitation: A Comparison between StandardGamble and Visual Analog Scale Methods. Twentieth Anniversary Meeting of the Society for Medical Decision Making (1998).
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)
desJardins, M., Getoor, L. & Koller, D. Using Feature Hierarchies in Bayesian Network Learning. Symposium on Abstraction, Reformulation and Approximation (2000).
Zheleva, E., Getoor, L., Golbeck, J. & Kuter, U. Using Friendship Ties and Family Circles for Link Prediction. 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD) (2008).PDF icon zheleva-snakdd08.pdf (656.31 KB)
Sridhar, D., Pujara, J. & Getoor, L. Using Noisy Extractions to Discover Causal Knowledge. NIPS Workshop on Automated Knowledge Base Construction (2017).PDF icon sridhar-akbc17.pdf (203.34 KB)
Getoor, L. & Sahami, M. Using Probabilistic Relational Models for Collaborative Filtering. Working Notes of the KDD Workshop on Web Usage Analysis and User Profiling (1999).
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)
Lerman, K., Getoor, L., Minton, S. & Knoblock, C. Using the Structure of Web Sites for Automatic Segmentation of Tables. In Proceedings of ACM-SIGMOD 2004 International Conference on Management of Data (2004).PDF icon lerman-sigmod04.pdf (307.43 KB)
Chajewska, U., Getoor, L., Norman, J. & Shahar, Y. Utility Elicitation as a Classi cation Problem. Uncertainty in Arti cial Intelligence (1998).
Chajewska, U., Getoor, L. & Norman, J. Utility Elicitation as a Classification Problem. Proceedings of the AAAI Spring Symposium Series on Interactive and Mixed Initiative Decision-Theoretic Systems (1998).
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Bilgic, M. & Getoor, L. VOILA: Efficient Feature-value Acquisition for Classification. AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence (2007).PDF icon bilgic-aaai07.pdf (220.47 KB)
Bilgic, M. & Getoor, L. Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition. Journal of Artificial Intelligence Research (JAIR) 41, 69–95 (2011).PDF icon bilgic11a.pdf (1.64 MB)
Kang, H., Getoor, L. & Singh, L. Visual Analysis of Dynamic Group Membership in Temporal Social Networks. SIGKDD Explorations, Special Issue on Visual Analytics 9, 13-21 (2007).PDF icon 2_kang-CGROUP_1207.pdf (1.48 MB)
Singh, L., Beard, M., Getoor, L. & M. Blake, B. Visual mining of multi-modal social networks at different abstraction levelsx. L. Singh, M. Beard, L. Getoor, M. Blake. Visual mining of multi-modal social networks at different abstraction levels. IEEE Conference on Information Visualization - Symposium of Visual Data Mining (IV-VDM) (2007).PDF icon singh2007IV.pdf (809.15 KB)
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Ramesh, A., Kumar, S., Foulds, J. & Getoor, L. Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums. 53rd Annual Meeting of the Association for Computational Linguistics (ACL) (2015).PDF icon ramesh-acl15.pdf (168.7 KB)
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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).

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