Publications

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P
Polymeropoulos, M. et al. Common effect of antipsychotics on the biosynthesis and regulation of fatty acids and cholesterol supports a key role of lipid homeostasis in schizophrenia. Schizophrenia Research (2009).
Pujara, J., Miao, H. & Getoor, L. Joint Judgments with a Budget: Strategies for Reducing the Cost of Inference. ICML Workshop on Machine Learning with Test-Time Budgets (2013).PDF icon pujara_wtbudg13.pdf (221.26 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Knowledge Graph Identification. International Semantic Web Conference (ISWC) (2013).PDF icon pujara_iswc13.pdf (508.7 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Large-Scale Knowledge Graph Identification using PSL. ICML Workshop on Structured Learning (SLG) (2013).PDF icon pujara_slg13.pdf (277.63 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Large-Scale Knowledge Graph Identification using PSL. AAAI Fall Symposium on Semantics for Big Data (2013).PDF icon pujara_s4bd13.pdf (306.96 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Ontology-Aware Partitioning for Knowledge Graph Identification. CIKM Workshop on Automatic Knowledge Base Construction (2013).PDF icon pujara_akbc13.pdf (370.62 KB)
Pujara, J. & Skomoroch, P. Large-Scale Hierarchical Topic Models. NIPS Workshop on Big Learning (2012).PDF icon pujara_biglearn12.pdf (189.96 KB)
Pujara, J., London, B. & Getoor, L. Reducing Label Cost by Combining Feature Labels and Crowdsourcing. ICML Workshop on Combining Learning Strategies to Reduce Label Cost (2011).PDF icon clsicml_pujara_london.pdf (253.29 KB)
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)
Pujara, J. & Getoor, L. Coarse-to-Fine, Cost-Sensitive Classification of E-Mail. NIPS Workshop on Coarse-to-Fine Processing (2010).PDF icon pujara_nips10.pdf (258.86 KB)
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 <https://github.com/eriq-augustine/meta-kg>PDF icon pujara-emnlp17.pdf (677.74 KB)
Pujara, J., London, B. & Getoor, L. Budgeted Online Collective Inference. Uncertainty in Artificial Intelligence (2015).PDF icon pujara-uai15.pdf (302.63 KB)
Pujara, J., London, B., Getoor, L. & Cohen, W. Online Inference for Knowledge Graph Construction. Workshop on Statistical Relational AI (2015).PDF icon pujara-starai15.pdf (340.95 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)
Pujara, J. Probabilistic Models for Scalable Knowledge Graph Construction. (2016).PDF icon pujara-thesis15.pdf (1.06 MB)
Pujara, J. & Getoor, L. Generic Statistical Relational Entity Resolution in Knowledge Graphs. Sixth International Workshop on Statistical Relational AI (IJCAI 2016, 2016).PDF icon pujara-starai16.pdf (151.37 KB)
Pujara, J. & Getoor, L. Building Dynamic Knowledge Graphs. NIPS Workshop on Automated Knowledge Base Construction (2014).PDF icon pujara_akbc14.pdf (143.26 KB)
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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)
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)
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)
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)
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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)
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)
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)
Sehgal, V., Getoor, L. & Viechnicki, P. Entity Resolution in Geospatial Data Integration. ACM GIS (2006).
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)

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