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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 <>PDF icon pujara-emnlp17.pdf (677.74 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)
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)
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)
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 <>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)
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)
Panagiotis, P., Panayiotis, T., Ariel, F. & Getoor, L. TACI: Taxonomy-Aware Catalog Integration. TKDE 25, (2012).PDF icon papadimitriou-tkde12.pdf (2.93 MB)
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)
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)
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)
Bradley, S. & Getoor, L. Topic Modeling for Wikipedia Link Disambiguation. ACM Transactions on Information Systems 32, (2014).
Embar, V., Srinivasan, S. & Getoor, L. Tractable Marginal Inference for Hinge-Loss Markov Random Fields. ICML Workshop on TPM (2019).
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)
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)
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).
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., 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., 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)
Getoor, L., Rhee, J., Koller, D. & Small, P. Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models. AI in Medicine Journal 30, 233-256 (2004).
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. ASONAM (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)
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)
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).
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)
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)