Publications

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Sharara, H., Halgin, D., Getoor, L. & Borgatti, S. Multi-dimensional Trajectory Analysis for Career Histories. International Sunbelt Social Networks Conference (Sunbelt XXXI) (2011).
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).
Sharara, H., Getoor, L. & Norton, M. Active Surveying. NIPS Workshop on Networks Across Disciplines in Theory and Applications (2010).
Sharara, H., Norton, M. & Getoor, L. Active Surveying for Leadership Identification. The International Sunbelt Social Networks Conference XXX (2010).
Sharara, H., Getoor, L. & Norton, M. An Active Learning Approach for Identifying Key Opinion Leaders. The 2nd Workshop on Information in Networks (WIN) (2010).
Sharara, H. & Getoor, L. Group Detection. Encyclopedia of Machine Learning (2010).
Sharara, H., Singh, L., Getoor, L. & Mann, J. The Dynamics of Actor Loyalty to Groups in Affiliation Networks. International Conference on Advances in Social Networks Analysis and Mining (2009).PDF icon sharara_asonam09.pdf (446.61 KB)
Sen, P., Namata, G. Mark, Bilgic, M. & Getoor, L. Collective Classification. Encyclopedia of Machine Learning (2010).
Sen, P., Deshpande, A. & Getoor, L. Read-Once Functions and Query Evaluation in Probabilistic Databases. International Conference on Very Large Data Bases (2010).PDF icon draft.pdf (322 KB)
Sen, P., Deshpande, A. & Getoor, L. Bisimulation-based Approximate Lifted Inference. Uncertainty in Artificial Intelligence (2009).PDF icon uai09.pdf (240.89 KB)
Sen, P., Deshpande, A. & 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)
Sen, P. Representing and Querying Uncertain Data. (2009).PDF icon thesis.pdf (1.12 MB)
Sen, P. et al. Collective Classification in Network Data. AI Magazine 29, 93–106 (2008).PDF icon sen-aimag08.pdf (497.82 KB)
Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. Data Mining and Knowledge Discovery, Special Issue on Utility Based Data Mining 17, 136–163 (2008).PDF icon draft.pdf (424.09 KB)
Sen, P., Deshpande, A. & Getoor, L. Exploiting Shared Correlations in Probabilistic Databases. International Conference on Very Large Data Bases (2008).PDF icon sen-vldb08.pdf (232.29 KB)
Sen, P. & Getoor, L. Link-based Classification. (2007).PDF icon senum-tr07.pdf (511.11 KB)
Sen, P. & Deshpande, A. Representing and Querying Correlated Tuples in Probabilistic Databases. International Conference on Data Engineering (2007).PDF icon icde07_final.pdf (309.63 KB)
Sen, P., Deshpande, A. & Getoor, L. Representing Tuple and Attribute Uncertainty in Probabilistic Databases. Workshop on Data Mining of Uncertain Data (ICDM) (2007).PDF icon dune07.pdf (176.67 KB)
Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. SIAM Data Mining Workshop on Link Analysis, Counterterrorism and Security (2006).PDF icon sensiam_lacs06.pdf (137.37 KB)
Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. International Conference on Machine Learning (2006).PDF icon senicml06.pdf (118.33 KB)
Sen, P. & Getoor, L. Empirical Comparison of Approximate Inference Algorithms for Networked Data. ICML Workshop on Statistical Relational Learning (SRL) (2006).PDF icon sensrl06.pdf (225.32 KB)
Sehgal, V., Getoor, L. & Viechnicki, P. Entity Resolution in Geospatial Data Integration. ACM GIS (2006).
Schnaitter, K., Polyzotis, N. & Getoor, L. Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications. International Conference on Very Large Data Bases (2009).PDF icon schnaitter-vldb09.pdf (743.29 KB)
Sayyadi, H. & Getoor, L. Future Rank: Ranking Scientific Articles by Predicting their Future PageRank. 2009 SIAM International Conference on Data Mining (SDM09) (2009).PDF icon sayyadi_futureRank_sdm09.pdf (621.55 KB)
Saha, B. & Getoor, L. On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch. 2009 SIAM International Conference on Data Mining (SDM09) (2009).PDF icon saha-sdm08.pdf (233.12 KB)
Saha, B. & Getoor, L. Group Proximity Measure for Recommending Groups in Online Social Networks. 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD) (2008).PDF icon kddw-saha.pdf (311.36 KB)
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Rekatsinas, T., Deshpande, A. & Getoor, L. Local Structure and Determinism in Probabilistic Databases. ACM SIGMOD Conference (2012).PDF icon sigmod_AAC2012.pdf (490.28 KB)
Rekatsinas, T., Dong, X. Luna, Getoor, L. & Srivastava, D. Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. 7th Biennial Conference on Innovative Data Systems Research (CIDR `15) (2015).PDF icon rekatsinasCIDR15.pdf (396.99 KB)
Rekatsinas, T. Quality-Aware Data Source Management. (2015).
Rekatsinas, T. et al. SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources. 2015 SIAM International Conference on Data Mining (SDM15) (SIAM, 2015).PDF icon rekatsinasSDM2015.pdf (303.08 KB)
Rekatsinas, T., Deshpande, A., Dong, X. Luna, Getoor, L. & Srivastava, D. SourceSight: Enabling Effective Source Selection. ACM SIGMOD Conference (2016).PDF icon modde087.pdf (799.94 KB)
Rekatsinas, T. et al. Forecasting Rare Disease Outbreaks from Open Source Indicators. Statistical Analysis and Data Mining: The ASA Data Science Journal (2016).PDF icon rekatsinas-sadm17.pdf (303.08 KB)
Rastegari, M., Choi, J., Fakhraei, S., III, H. Daume & Davis, L. Predictable Dual-View Hashing. Proceedings of the 30th International Conference on Machine Learning (ICML-13) 1328–1336 (JMLR, 2013).PDF icon rastegari13.pdf (2.35 MB)
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Learning Latent Engagement Patterns of Students in Online Courses. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI Press, 2014).PDF icon ramesh-aaai14.pdf (505.47 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).
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)
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic. NIPS Workshop on Data Driven Education (2013).PDF icon ramesh-nipsws13.pdf (153.92 KB)
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)
Ramesh, A., Rodriguez, M. & Getoor, L. Multi-relational influence models for online professional networks. International Conference on Web Intelligence (ICWI) 291-298 (ACM, 2017).PDF icon ramesh-icwi17.pdf (761.17 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., 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)
Ramesh, A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. (2016).PDF icon ramesh-thesis16.pdf (865.41 KB)
Ramesh, A. & Getoor, L. Understanding Evolution of Long-running MOOCs. International Conference on Web Information Systems Engineering (WISE) (2018).
Ramesh, A., Goldwasser, D., Huang, B., Daume-III, H. & Getoor, L. Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields. Transactions on Learning Technologies (2019).PDF icon ramesh-tlt19.pdf (4.3 MB)
Ramakrishnan, N. et al. ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2014).PDF icon ramakrishnan-kdd14.pdf (1.15 MB)

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