Publications

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P. Sen, Deshpande, A., and Getoor, L., PrDB: Managing and Exploiting Rich Correlations in Probabilistic Databases, VLDB Journal, special issue on uncertain and probabilistic databases, 2009.PDF icon sen-vldbj09.pdf (1.12 MB)
P. Sen, Representing and Querying Uncertain Data, University of Maryland, College Park, 2009.PDF icon thesis.pdf (1.12 MB)
P. Sen, Namata, G. Mark, Bilgic, M., Getoor, L., Gallagher, B., and Eliassi-Rad, T., Collective Classification in Network Data, AI Magazine, vol. 29, pp. 93–106, 2008.PDF icon sen-aimag08.pdf (497.82 KB)
P. Sen and Getoor, L., Cost-Sensitive Learning with Conditional Markov Networks, Data Mining and Knowledge Discovery, Special Issue on Utility Based Data Mining, vol. 17, pp. 136–163, 2008.PDF icon draft.pdf (424.09 KB)
P. Sen, Deshpande, A., and Getoor, L., Exploiting Shared Correlations in Probabilistic Databases, in International Conference on Very Large Data Bases, 2008.PDF icon sen-vldb08.pdf (232.29 KB)
P. Sen and Getoor, L., Link-based Classification. University of Maryland, 2007.PDF icon senum-tr07.pdf (511.11 KB)
P. Sen and Deshpande, A., Representing and Querying Correlated Tuples in Probabilistic Databases, in International Conference on Data Engineering, 2007.PDF icon icde07_final.pdf (309.63 KB)
P. Sen, Deshpande, A., and Getoor, L., Representing Tuple and Attribute Uncertainty in Probabilistic Databases, in Workshop on Data Mining of Uncertain Data (ICDM), 2007.PDF icon dune07.pdf (176.67 KB)
P. Sen and Getoor, L., Cost-Sensitive Learning with Conditional Markov Networks, in SIAM Data Mining Workshop on Link Analysis, Counterterrorism and Security, 2006.PDF icon sensiam_lacs06.pdf (137.37 KB)
P. Sen and Getoor, L., Cost-Sensitive Learning with Conditional Markov Networks, in International Conference on Machine Learning, 2006.PDF icon senicml06.pdf (118.33 KB)
P. Sen and Getoor, L., Empirical Comparison of Approximate Inference Algorithms for Networked Data, in ICML Workshop on Statistical Relational Learning (SRL), 2006.PDF icon sensrl06.pdf (225.32 KB)
H. Sharara, Singh, L., and Getoor, L., Finding Prominent Actors in Dynamic Affiliation Networks, Human Journal, 2012.PDF icon 105-204-1-SM.pdf (757.7 KB)
H. Sharara, Rand, W., and Getoor, L., Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing, in ICWSM, 2011.PDF icon sharara-aaai11.pdf (646.6 KB)
H. Sharara, Sopan, A., Namata, G. Mark, Getoor, L., and Singh, L., G-PARE: A Visual Analytic Tool for Comparative Analysis of Uncertain Graphs, in IEEE Conference on Visual Analytics Science and Technology (VAST), 2011.PDF icon sharara-vast11.pdf (1.64 MB)
H. Sharara, Halgin, D., Getoor, L., and Borgatti, S., Multi-dimensional Trajectory Analysis for Career Histories, in International Sunbelt Social Networks Conference (Sunbelt XXXI), 2011.
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.
H. Sharara, Getoor, L., and Norton, M., Active Surveying, in NIPS Workshop on Networks Across Disciplines in Theory and Applications, 2010.
H. Sharara, Norton, M., and Getoor, L., Active Surveying for Leadership Identification, in The International Sunbelt Social Networks Conference XXX, 2010.
H. Sharara, Getoor, L., and Norton, M., An Active Learning Approach for Identifying Key Opinion Leaders, in The 2nd Workshop on Information in Networks (WIN), 2010.
H. Sharara and Getoor, L., Group Detection, Encyclopedia of Machine Learning, 2010.
H. Sharara, Singh, L., Getoor, L., and Mann, J., The Dynamics of Actor Loyalty to Groups in Affiliation Networks, in International Conference on Advances in Social Networks Analysis and Mining, 2009.PDF icon sharara_asonam09.pdf (446.61 KB)
L. Singh and Getoor, L., Increasing the predictive power of affiliation networks., IEEE Data Engineering Bulletin, vol. 30, 2007.PDF icon singh.pdf (87.94 KB)
L. Singh, Beard, M., Getoor, L., and M. Blake, B., Visual mining of multi-modal social networks at different abstraction levelsx, in 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)
L. Singh, Getoor, L., and Licamele, L., Pruning Social Networks Using Structural Properties and Descriptive Attributes, in IEEE International Conference on Data Mining (ICDM), 2005, pp. 773-776.PDF icon singh_icdm05.pdf (149.45 KB)
M. Smith, Barash, V., Getoor, L., and Lauw, H., Leveraging Social Context for Searching Social Media, in CIKM Workshop on Search in Social Media, 2008.
S. Somasundaran, Namata, G. Mark, Getoor, L., and Wiebe, J., Opinion Graphs for Polarity and Discourse Classification, in TextGraphs-4: Graph-based Methods for Natural Language Processing, 2009.PDF icon somasundaran-textgraphs09.pdf (289.75 KB)
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)
D. Sridhar, Getoor, L., and Walker, M., Collective Stance Classification of Posts in Online Debate Forums, in ACL Joint Workshop on Social Dynamics and Personal Attributes in Social Media, 2014.PDF icon sridhar-aclws14.pdf (190.8 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)
D. Sridhar, Foulds, J., Walker, M., Huang, B., and Getoor, L., Joint Models of Disagreement and Stance in Online Debate, in Annual Meeting of the Association for Computational Linguistics (ACL), 2015.PDF icon sridhar-acl15.pdf (227.14 KB)
D. Sridhar and Getoor, L., Joint Probabilistic Inference of Causal Structure, in KDD Workshop on CD, 2016.PDF icon sridhar-cd16.pdf (204.51 KB)
D. Sridhar and Getoor, L., Probabilistic Inference for Causal Structure Discovery, in UAI Workshop on Causation, 2016.PDF icon sridhar-causation16.pdf (118.31 KB)
D. Sridhar, Fakhraei, S., and Getoor, L., A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction, Bioinformatics, vol. 32, 2016.PDF icon sridhar-bioinformatics16.pdf (1.94 MB)
D. Sridhar, Foulds, J., Huang, B., Walker, M., and Getoor, L., Collective classification of stance and disagreement in online debate forums, in Bay Area Machine Learning Symposium (BayLearn), 2014.
D. Sridhar, Pujara, J., and Getoor, L., Scalable Probabilistic Causal Structure Discovery, in International Joint Conference on Artificial Intelligence (IJCAI), 2018.PDF icon sridhar-ijcai18.pdf (281.32 KB)
D. Sridhar, Springer, A., Hollis, V., Whittaker, S., and Getoor, L., Estimating Causal Effects of Exercise from Mood Logging Data, in ICML Workshop on Causal Machine Learning (CausalML), 2018.PDF icon sridhar-causalml18.pdf (333.69 KB)
D. Sridhar and Getoor, L., Estimating Causal Effects of Tone in Online Debates, in International Joint Conference on Artificial Intelligence (IJCAI), 2019.PDF icon sridhar-ijcai19.pdf (220.49 KB)
S. Srinivasan, Babaki, B., Farnadi, G., and Getoor, L., Lifted Hinge-Loss Markov Random Fields, in AAAI Conference on Artificial Intelligence (AAAI), 2019.PDF icon srinivasan-aaai19.pdf (417.5 KB)
S. Srinivasan, Rao, N. S., Subbaian, K., and Getoor, L., Identifying Facet Mismatches In Search Via Micrographs, in International Conference on Information and Knowledge Management (CIKM), 2019.PDF icon srinivasan-cikm19.pdf (887.06 KB)
S. Srinivasan, Farnadi, G., and Getoor, L., BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic, in AAAI Conference on Artificial Intelligence (AAAI), 2020.PDF icon srinivasan-aaai20a.pdf (478.65 KB)
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)
T
S. Tomkins, Getoor, L., Chen, Y., and Zhang, Y., Detecting Cyber-bullying from Sparse Data and Inconsistent Labels, in Learning with Limited Labeled Data (LLD) NIPS Workshop, 2017.PDF icon tomkins-NIPSLLD17.pdf (286.95 KB)
S. Tomkins, Pujara, J., and Getoor, L., Disambiguating Energy Disaggregation: A Collective Probabilistic Approach, in International Joint Conference on Artifi cial Intelligence, 2017.PDF icon tomkins-ijcai17.pdf (373.28 KB)
S. Tomkins, Ramesh, A., and Getoor, L., Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study, in EDM, 2016.PDF icon tomkins-edm16.pdf (619.77 KB)
S. Tomkins, Getoor, L., Chen, Y., and Zhang, Y., A Socio-linguistic Model for Cyberbullying Detection, in International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018.PDF icon tomkins-asonam18.pdf (299.34 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, 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)
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)
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)
U
O. Udrea and Getoor, L., Combining statistical and logical inference for ontology alignment, in Workshop on Semantic Web for Collaborative Knowledge Acquisition at the International Joint Conference on Artificial Intelligence, 2007.

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