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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).
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)
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)
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)
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)
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)
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).
Chajewska, U., Getoor, L., Norman, J. & Shahar, Y. Utility Elicitation as a Classi cation Problem. Uncertainty in Arti cial Intelligence (1998).
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)
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)
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).
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)
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)
desJardins, M., Getoor, L. & Koller, D. Using Feature Hierarchies in Bayesian Network Learning. Symposium on Abstraction, Reformulation and Approximation (2000).
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)
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).
Arti, R., Jaebong, Y., Shitian, S., Getoor, L. & Jihie, K. User Role Prediction in Online Discussion Forums using Probabilistic Soft Logic. NeuRIPS Workshop on PE (2012).PDF icon ramesh-pe11.pdf (40.65 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)
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)
Kumar, S. et al. Unsupervised Models for Predicting Strategic Relations between Organizations. ASONAM (2016).PDF icon kumar-asonam16.pdf (212.61 KB)
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)
Getoor, L., Rhee, J., Koller, D. & Small, P. Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models. AI in Medicine Journal 30, 233-256 (2004).
Ramesh, A., Goldwasser, D., Huang, B., Daume, III, H. & Getoor, L. Understanding MOOC Discussion Forums using Seeded LDA. ACL Workshop on Innovative Use of NLP for Building Educational Applications (ACL, 2014).PDF icon ramesh-aclws14.pdf (137.57 KB)
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)
Tomkins, S. & Getoor, L. Understanding Hybrid-MOOC Effectiveness with a Collective Socio-Behavioral Model. JEDM 11, 42--77 (2019).PDF icon tomkins-jedm19.pdf (679.09 KB)
Ramesh, A. & Getoor, L. Understanding Evolution of Long-running MOOCs. International Conference on Web Information Systems Engineering (WISE) (2018).
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., 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).
Zheleva, E., Kolcz, A. & Getoor, L. Trusting Spam Reporters: A Reporter-based Reputation System for Email Filtering. ACM Transactions on Information Systems 27, (2008).PDF icon zheleva-tois08.pdf (447.31 KB)
Augustine, E., Rekatsinas, T. & Getoor, L. Tractable Probabilistic Reasoning Through Effective Grounding. ICML Workshop on TPM (2019).PDF icon augustine-tpm19.pdf (224.8 KB)
Embar, V., Srinivasan, S. & Getoor, L. Tractable Marginal Inference for Hinge-Loss Markov Random Fields. ICML Workshop on TPM (2019).
Bradley, S. & Getoor, L. Topic Modeling for Wikipedia Link Disambiguation. ACM Transactions on Information Systems 32, (2014).
Zheleva, E. & Getoor, L. To Join or not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles. 18th International World Wide Web conference (WWW) (2009).PDF icon fp660-zheleva.pdf (538.92 KB)
Zheleva, E. & Getoor, L. To Join or not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles. The Web Conference (WWW) (University of Maryland, 2009).PDF icon zheleva-cs-tr4926.pdf (366.68 KB)
London, B., Huang, B. & Getoor, L. The Benefits of Learning with Strongly Convex Approximate Inference. ICML (2015).PDF icon london-icml15.pdf (788.06 KB)
Srinivasan, S., Augustine, E. & Getoor, L. Tandem Inference: An Out-of-Core Streaming Algorithm For Very Large-Scale Relational Inference. Association for the Advancement of Artificial Intelligence (2020).PDF icon srinivasan-aaai20b.pdf (506.62 KB)
Panagiotis, P., Panayiotis, T., Ariel, F. & Getoor, L. TACI: Taxonomy-Aware Catalog Integration. TKDE 25, (2012).PDF icon papadimitriou-tkde12.pdf (2.93 MB)
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)
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)
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)
Dietterich, T., Domingos, P., Getoor, L., Muggleton, S. & Tadepalli, P. Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008).
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 <>PDF icon zhang-www18.pdf (800.66 KB)
Getoor, L. Structure Discovery Using Statistical Relational Learning. Data Engineering Bulletin 26, 11- -18 (2003).
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)
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)
Hossam, S., Lisa, S., Getoor, L. & Janet, M. Stability vs. Diversity: Understanding the Dynamics of Actors in Time-varying Affiliation Networks. ICSI (2012).PDF icon sharara-icsi12.pdf (307.98 KB)
London, B., Huang, B. & Getoor, L. Stability and Generalization in Structured Prediction. (2015).PDF icon london-stability15.pdf (532.16 KB)
London, B., Huang, B. & Getoor, L. Stability and Generalization in Structured Prediction. Journal of Machine Learning Research 17, (2016).PDF icon london-jmlr17.pdf (532.8 KB)
Islamaj, R., Getoor, L., W. Wilbur, J. & Mount, S. SplicePort - An interactive splice-site analysis tool. Nucleic Acids Research (2007).PDF icon dogan-nar.pdf (1.34 MB)