Publications

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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)
G. Piatetsky-Shapiro, Grossman, R., Djeraba, C., Feldman, R., Getoor, L., and Zaki, M., Is there a grand challenge or X-prize for data mining?, in 12th International Conference on Knowledge Discovery and Data Mining, 2006.
A. Plangprasopchok, Lerman, K., and Getoor, L., A Probabilistic Approach for Learning Folksonomies from Structured Data, in Fourth ACM International Conference on Web Search and Data Mining (WSDM), 2011.
A. Plangprasopchok, Lerman, K., and Getoor, L., Growing a tree in the forest: constructing folksonomies by integrating structured metadata, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.PDF icon plang-kdd10.pdf (705.71 KB)
M. Polymeropoulos, Licamele, L., Volpi, S., Mack, K., Mitkus, S., Carstea, E., Getoor, L., and Lavedan, C., 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.
J. Pujara, Miao, H., and Getoor, L., Joint Judgments with a Budget: Strategies for Reducing the Cost of Inference, in ICML Workshop on Machine Learning with Test-Time Budgets, 2013.PDF icon pujara_wtbudg13.pdf (221.26 KB)
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Knowledge Graph Identification, in International Semantic Web Conference (ISWC), 2013.PDF icon pujara_iswc13.pdf (508.7 KB)
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Large-Scale Knowledge Graph Identification using PSL, in ICML Workshop on Structured Learning (SLG), 2013.PDF icon pujara_slg13.pdf (277.63 KB)
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Large-Scale Knowledge Graph Identification using PSL, in AAAI Fall Symposium on Semantics for Big Data, 2013.PDF icon pujara_s4bd13.pdf (306.96 KB)
J. Pujara, Miao, H., Getoor, L., and Cohen, W., Ontology-Aware Partitioning for Knowledge Graph Identification, in CIKM Workshop on Automatic Knowledge Base Construction, 2013.PDF icon pujara_akbc13.pdf (370.62 KB)
J. Pujara and Skomoroch, P., Large-Scale Hierarchical Topic Models, in NIPS Workshop on BigLearn, 2012.PDF icon pujara_biglearn12.pdf (189.96 KB)
J. Pujara, London, B., and Getoor, L., Reducing Label Cost by Combining Feature Labels and Crowdsourcing, in ICML Workshop on Combining Learning Strategies to Reduce Label Cost, 2011.PDF icon clsicml_pujara_london.pdf (253.29 KB)
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)
J. Pujara and Getoor, L., Coarse-to-Fine, Cost-Sensitive Classification of E-Mail, in NIPS Workshop on Coarse-to-Fine Processing, 2010.PDF icon pujara_nips10.pdf (258.86 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)
J. Pujara, London, B., and Getoor, L., Budgeted Online Collective Inference, in UAI, 2015.PDF icon pujara-uai15.pdf (302.63 KB)
J. Pujara, London, B., Getoor, L., and Cohen, W., Online Inference for Knowledge Graph Construction., in Workshop on Statistical Relational AI, 2015.PDF icon pujara-starai15.pdf (340.95 KB)
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)
J. Pujara, Probabilistic Models for Scalable Knowledge Graph Construction, University of Maryland, College Park, 2016.PDF icon pujara-thesis15.pdf (1.06 MB)
J. Pujara and Getoor, L., Generic Statistical Relational Entity Resolution in Knowledge Graphs, in StarAI, 2016.PDF icon pujara-starai16.pdf (151.37 KB)
J. Pujara and Getoor, L., Building Dynamic Knowledge Graphs, in NIPS Workshop on Automated Knowledge Base Construction, 2014.PDF icon pujara_akbc14.pdf (143.26 KB)
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N. Ramakrishnan, Butler, P., Self, N., Khandpur, R., Saraf, P., Wang, W., Cadena, J., Vullikanti, A., Korkmaz, G., Kuhlman, C., Marathe, A., Zhao, L., Ting, H., Huang, B., Srinivasan, A., Trinh, K., Getoor, L., Katz, G., Doyle, A., Ackermann, C., Zavorin, I., Ford, J., Summers, K., Fayed, Y., Arredondo, J., Gupta, D., and Mares, D., ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators, in ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2014.PDF icon ramakrishnan-kdd14.pdf (1.15 MB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Learning Latent Engagement Patterns of Students in Online Courses, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.PDF icon ramesh-aaai14.pdf (505.47 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.
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)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic, in NIPS Workshop on Data Driven Education, 2013.PDF icon ramesh-nipsws13.pdf (153.92 KB)
A. Ramesh, Rodriguez, M., and Getoor, L., Multi-relational influence models for online professional networks, in International Conference on Web Intelligence (ICWI), 2017, pp. 291-298.PDF icon ramesh-icwi17.pdf (761.17 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, Kumar, S., Foulds, J., and Getoor, L., Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums, in 53rd Annual Meeting of the Association for Computational Linguistics (ACL), 2015.PDF icon ramesh-acl15.pdf (168.7 KB)
A. Ramesh, A Probabilistic Approach to Modeling Socio-Behavioral Interactions, University of Maryland, College Park, 2016.PDF icon ramesh-thesis16.pdf (865.41 KB)
A. Ramesh and Getoor, L., Understanding Evolution of Long-running MOOCs, in International Conference on Web Information Systems Engineering (WISE), 2018.
A. Ramesh, Goldwasser, D., Huang, B., Daume, III, H., and Getoor, L., Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields, IEEE Transactions on Learning Technologies (TLT), vol. 14, pp. 1-1, 2019.PDF icon ramesh-tlt19.pdf (4.3 MB)
M. Rastegari, Choi, J., Fakhraei, S., III, H. Daume, and Davis, L., Predictable Dual-View Hashing, in Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013, pp. 1328–1336.PDF icon rastegari13.pdf (2.35 MB)
T. Rekatsinas, Deshpande, A., and Getoor, L., Local Structure and Determinism in Probabilistic Databases, in SIGMOD, 2012.PDF icon rekatsinas-sigmod12.pdf (490.28 KB)
T. Rekatsinas, Dong, X. Luna, Getoor, L., and Srivastava, D., Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration, in 7th Biennial Conference on Innovative Data Systems Research (CIDR `15), 2015.PDF icon rekatsinasCIDR15.pdf (396.99 KB)
T. Rekatsinas, Quality-Aware Data Source Management, University of Maryland, College Park, 2015.
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)
T. Rekatsinas, Ghosh, S., Mekaru, S., Nsoesie, E., Brownstein, J., Getoor, L., and Ramakrishnan, N., Forecasting Rare Disease Outbreaks Using Multiple Data Sources, STAT ANAL DATA MIN, 2015.
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B. Saha and Getoor, L., On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch, in 2009 SIAM International Conference on Data Mining (SDM09), 2009.PDF icon saha-sdm08.pdf (233.12 KB)
B. Saha and Getoor, L., Group Proximity Measure for Recommending Groups in Online Social Networks, in 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD), 2008.PDF icon kddw-saha.pdf (311.36 KB)
B. Salami, Parikh, H., Kayali, M., Roy, S., Getoor, L., and Suciu, D., Causal Relational Learning, in International Conference on Management of Data (SIGMOD), 2020.PDF icon salami-sigmod20.pdf (1.02 MB)
H. Sayyadi and Getoor, L., Future Rank: Ranking Scientific Articles by Predicting their Future PageRank, in 2009 SIAM International Conference on Data Mining (SDM09), 2009.PDF icon sayyadi_futureRank_sdm09.pdf (621.55 KB)
K. Schnaitter, Polyzotis, N., and Getoor, L., Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications, in International Conference on Very Large Data Bases, 2009.PDF icon schnaitter-vldb09.pdf (743.29 KB)
V. Sehgal, Getoor, L., and Viechnicki, P., Entity Resolution in Geospatial Data Integration, in ACM GIS, 2006.
P. Sen, Namata, G. Mark, Bilgic, M., and Getoor, L., Collective Classification, Encyclopedia of Machine Learning, 2010.
P. Sen, Deshpande, A., and Getoor, L., Read-Once Functions and Query Evaluation in Probabilistic Databases, in International Conference on Very Large Data Bases, 2010.PDF icon draft.pdf (322 KB)
P. Sen, Deshpande, A., and Getoor, L., Bisimulation-based Approximate Lifted Inference, in Uncertainty in Artificial Intelligence, 2009.PDF icon uai09.pdf (240.89 KB)

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