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T. Rekatsinas, Ghosh, S., Mekaru, S., Nsoesie, E., Brownstein, J., Getoor, L., and Ramakrishnan, N., SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources, in 2015 SIAM International Conference on Data Mining (SDM15), 2015.PDF icon rekatsinasSDM2015.pdf (303.08 KB)
T. Rekatsinas, Deshpande, A., Dong, L., Getoor, L., and Srivastava, D., SourceSight: Enabling Effective Source Selection, in SIGMOD, 2016.PDF icon rekatsinas-sigmod16.pdf (799.94 KB)
J. Pujara, Augustine, E., and Getoor, L., Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short, in Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.PDF icon pujara-emnlp17.pdf (677.74 KB)
R. Islamaj, Getoor, L., W. Wilbur, J., and Mount, S., SplicePort - An interactive splice-site analysis tool, Nucleic Acids Research, 2007.PDF icon dogan-nar.pdf (1.34 MB)
B. London, Huang, B., and Getoor, L., Stability and Generalization in Structured Prediction, , 2015.PDF icon london-stability15.pdf (532.16 KB)
B. London, Huang, B., and Getoor, L., Stability and Generalization in Structured Prediction, Journal of Machine Learning Research, vol. 17, 2016.PDF icon london-jmlr17.pdf (532.8 KB)
S. Hossam, Lisa, S., Getoor, L., and Janet, M., Stability vs. Diversity: Understanding the Dynamics of Actors in Time-varying Affiliation Networks, in ICSI, 2012.PDF icon sharara-icsi12.pdf (307.98 KB)
G. Farnadi, Bach, S. H., Blondeel, M., Moens, M. - F., Getoor, L., and De Cock, M., Statistical Relational Learning with Soft Quantifiers, in International Conference on Inductive Logic Programming (ILP), 2015.PDF icon farnadi-ilp15.pdf (578.43 KB)
B. London, Huang, B., and Getoor, L., On the Strong Convexity of Variational Inference, in NIPS Workshop on Advances in Variational Inference, 2014.PDF icon london-nips14ws.pdf (253.72 KB)
L. Getoor, Structure Discovery Using Statistical Relational Learning, Data Engineering Bulletin, vol. 26, p. 11- -18, 2003.
T. Dietterich, Domingos, P., Getoor, L., Muggleton, S., and Tadepalli, P., Structured machine learning: the next ten years, Machine Learning, vol. 73, pp. 3–23, 2008.
W. Eldin Moustafa, Kimmig, A., Deshpande, A., and Getoor, L., Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty, in International Conference on Data Engineering (ICDE), 2014.PDF icon ICDE14_conf_full_374.pdf (1.57 MB)
S. Somasundaran, Namata, G. Mark, Wiebe, J., and Getoor, L., Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification, in Conference on Empirical Methods in Natural Language Processing, 2009.PDF icon somasundaran-emnlp09.pdf (390.93 KB)
S. Tomkins, Isley, S., London, B., and Getoor, L., Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations, in Recommender Systems (RecSys), 2018.PDF icon tomkins-recsys18.pdf (655.92 KB)
P. Panagiotis, Panayiotis, T., Ariel, F., and Getoor, L., TACI: Taxonomy-Aware Catalog Integration, TKDE, vol. 25, 2012.PDF icon papadimitriou-tkde12.pdf (2.93 MB)
S. Srinivasan, Augustine, E., and Getoor, L., Tandem Inference: An Out-of-Core Streaming Algorithm For Very Large-Scale Relational Inference, in AAAI Conference on Artificial Intelligence (AAAI), 2020.PDF icon srinivasan-aaai20b.pdf (506.62 KB)
B. London, Huang, B., and Getoor, L., The Benefits of Learning with Strongly Convex Approximate Inference, in ICML, 2015.PDF icon london-icml15.pdf (788.06 KB)
S. Tomkins, Farnadi, G., Amantullah, B., Getoor, L., and Minton, S., The Impact of Environmental Stressors on Human Trafficking, in ICWSM Workshop on Beyond Online Data (BOD), 2018.PDF icon tomkins-bod18.pdf (473.58 KB)
S. Tomkins, Farnadi, G., Amantullah, B., Getoor, L., and Minton, S., The Impact of Environmental Stressors on Human Trafficking, in International Conference on Data Mining (ICDM), 2018.PDF icon tomkins-icdm18.pdf (473.58 KB)
E. Zheleva and Getoor, L., To Join or not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles, in 18th International World Wide Web conference (WWW), 2009.PDF icon fp660-zheleva.pdf (538.92 KB)
E. Zheleva and Getoor, L., To Join or not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles, in The Web Conference (WWW), College Park, 2009.PDF icon zheleva-cs-tr4926.pdf (366.68 KB)
S. Bradley and Getoor, L., Topic Modeling for Wikipedia Link Disambiguation, ACM Transactions on Information Systems, vol. 32, 2014.
V. Embar, Srinivasan, S., and Getoor, L., Tractable Marginal Inference for Hinge-Loss Markov Random Fields, in ICML Workshop on Tractable Probabilistic Modeling (TPM), 2019.PDF icon embar-tpm19.pdf (410.24 KB)
E. Augustine, Rekatsinas, T., and Getoor, L., Tractable Probabilistic Reasoning Through Effective Grounding, in ICML Workshop on Tractable Probabilistic Modeling (TPM), 2019.PDF icon augustine-tpm19.pdf (224.8 KB)
E. Zheleva, Kolcz, A., and Getoor, L., Trusting Spam Reporters: A Reporter-based Reputation System for Email Filtering, ACM Transactions on Information Systems, vol. 27, 2008.PDF icon zheleva-tois08.pdf (447.31 KB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs, in ACM Conference on Learning at Scale, 2014.
H. Sharara, Singh, L., Getoor, L., and Mann, J., Understanding Actor Loyalty to Event-Based Groups in Affiliation Networks, Journal of Advances in Social Networks Analysis and Mining, vol. 1, pp. 115–126, 2011.
A. Ramesh and Getoor, L., Understanding Evolution of Long-running MOOCs, in International Conference on Web Information Systems Engineering (WISE), 2018.
S. Tomkins and Getoor, L., Understanding Hybrid-MOOC Effectiveness with a Collective Socio-Behavioral Model, Journal of Educational Data Mining (JEDM), vol. 11, p. 42--77, 2019.PDF icon tomkins-jedm19.pdf (679.09 KB)
A. Ramesh, Rodriguez, M., and Getoor, L., Understanding Influence in Online Professional Networks, in NIPS Workshop on Networks in Social and Information Sciences, 2015.PDF icon ramesh-nipsws15.pdf (211.44 KB)
A. Ramesh, Goldwasser, D., Huang, B., Daume, III, H., and Getoor, L., Understanding MOOC Discussion Forums using Seeded LDA, in ACL Workshop on Innovative Use of NLP for Building Educational Applications, 2014.PDF icon ramesh-aclws14.pdf (137.57 KB)
L. Getoor, Rhee, J., Koller, D., and Small, P., Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models, AI in Medicine Journal, vol. 30, pp. 233-256, 2004.
S. H. Bach, Huang, B., and Getoor, L., Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees, in Artificial Intelligence and Statistics (AISTATS), 2015.PDF icon bach-aistats15.pdf (345.2 KB)
S. Kumar, Pujara, J., Getoor, L., Mares, D., Gupta, D., and Riloff, E., Unsupervised Models for Predicting Strategic Relations between Organizations, in ASONAM, 2016.PDF icon kumar-asonam16.pdf (212.61 KB)
I. Bhattacharya, Getoor, L., and Bengio, Y., Unsupervised Sense Disambiguation using Bilingual Probabilistic Models, in Annual Meeting of the Association for Computational Linguistics (ACL), 2004.PDF icon acl04.pdf (156.26 KB)
P. Kouki, Schaffer, J., Pujara, J., ODonovan, J., and Getoor, L., User Preferences for Hybrid Explanations, in 11th ACM Conference on Recommender Systems (RecSys), 2017.PDF icon kouki-recsys17.pdf (2.64 MB)
R. Arti, Jaebong, Y., Shitian, S., Getoor, L., and Jihie, K., User Role Prediction in Online Discussion Forums using Probabilistic Soft Logic, in NeuRIPS Workshop on PE, 2012.PDF icon ramesh-pe11.pdf (40.65 KB)
U. Chajewska, Norman, J., and Getoor, L., Using Classi cation Techniques for Utility Elicitation: A Comparison between StandardGamble and Visual Analog Scale Methods, in Twentieth Anniversary Meeting of the Society for Medical Decision Making, 1998.
J. Pujara, III, H. Daume, and Getoor, L., Using Classifier Cascades for Scalable E-Mail Classification, in Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, 2011.PDF icon pujara_ceas2011_camera.pdf (308.42 KB)
M. desJardins, Getoor, L., and Koller, D., Using Feature Hierarchies in Bayesian Network Learning, in Symposium on Abstraction, Reformulation and Approximation, 2000.
E. Zheleva, Getoor, L., Golbeck, J., and Kuter, U., Using Friendship Ties and Family Circles for Link Prediction, in 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD), 2008.PDF icon zheleva-snakdd08.pdf (656.31 KB)
D. Sridhar, Pujara, J., and Getoor, L., Using Noisy Extractions to Discover Causal Knowledge, in NIPS Workshop on Automated Knowledge Base Construction, 2017.PDF icon sridhar-akbc17.pdf (203.34 KB)
L. Getoor and Sahami, M., Using Probabilistic Relational Models for Collaborative Filtering, in Working Notes of the KDD Workshop on Web Usage Analysis and User Profiling, 1999.
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Using Semantics & Statistics to Turn Data into Knowledge, AI Magazine, vol. 36, pp. 65–74, 2015.PDF icon pujara_aimag15.pdf (359.48 KB)
K. Lerman, Getoor, L., Minton, S., and Knoblock, C., Using the Structure of Web Sites for Automatic Segmentation of Tables, in In Proceedings of ACM-SIGMOD 2004 International Conference on Management of Data, 2004.PDF icon lerman-sigmod04.pdf (307.43 KB)
U. Chajewska, Getoor, L., Norman, J., and Shahar, Y., Utility Elicitation as a Classi cation Problem, in Uncertainty in Arti cial Intelligence, 1998.
U. Chajewska, Getoor, L., and Norman, J., Utility Elicitation as a Classification Problem, in Proceedings of the AAAI Spring Symposium Series on Interactive and Mixed Initiative Decision-Theoretic Systems, 1998.
M. Bilgic and Getoor, L., VOILA: Efficient Feature-value Acquisition for Classification, in AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence, 2007.PDF icon bilgic-aaai07.pdf (220.47 KB)
M. Bilgic and Getoor, L., Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition, Journal of Artificial Intelligence Research (JAIR), vol. 41, pp. 69–95, 2011.PDF icon bilgic11a.pdf (1.64 MB)
H. Kang, Getoor, L., and Singh, L., Visual Analysis of Dynamic Group Membership in Temporal Social Networks, SIGKDD Explorations, Special Issue on Visual Analytics, vol. 9, pp. 13-21, 2007.PDF icon 2_kang-CGROUP_1207.pdf (1.48 MB)


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