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Author anyAckermann, Chris Aggarwal, Charu Amantullah, Brian Arredondo, Jaime Augustine, Eriq Babaki, Behrouz Bach, Stephen H.Barash, Vladimir Beard, Mitchell Bengio, Yoshua Bhattacharya, Indrajit Bilgic, Mustafa Blake, Brian Blondeel, Marjon Borgatti, Steve Boyd-Graber, Jordan Bradley, Skaggs Broecheler, Matthias Brownstein, John Butler, Patrick Cadena, Jose Carlson, Bjorn Carstea, Eugene Chajewska, Ursulza Chang, Johnnie Chen, Daozheng Chen, Yunfei Chen, Robert Choi, Jonghyun Cohen, William Cook, D. Daume-III, Hal Davis, Larry De Cock, Martine Deshpande, Amol Diehl, Christopher Dietterich, Thomas Djeraba, Chabane Domingos, Pedro Dong, Xin LunaDoppa, Janardhan Doyle, Andy Dzeroski, S. Eirinaki, Magdalini Eliassi-Rad, Tina Elsayed, Tamer Embar, Varun Fakhraei, Shobeir Faloutsos, Christos Farnadi, Golnoosh Fayed, Youssef Feldman, Ronen Ford, Jim Foulds, James Friedman, Nir Friedman, Mark Fromherz, Markus Fuxman, Ariel Gallagher, Brian Gazen, Bora C.Getoor, Lise Ghosh, Saurav Golbeck, Jennifer Goldwasser, Dan Grant, John Grossman, Robert Grycner, Adam Guiver, John Gupta, Dipak Haidarian-Shahri, Hamid Halgin, Daniel He, Xinran Healy, Patrick Holder, L. Hollis, Victoria Huang, Bert Hung, Edward Hwang, Heasoo III, Hal DaumeIslamaj, Rezarta Isley, Steve Jacobs, David Jr., Nick ShortKang, Jeonhyung Kang, Hyunmo Katz, Graham Khamis, Sameh Khandpur, Rupinder Kim, Jihie Kim, Sungchul Kimmig, Angelika Kini, Nikhil Knoblock, Craig Koehly, Laura Koh, Eunyee Kok, Stanley Kolcz, Alek Koller, Daphne Korkmaz, Gizem Kouki, Pigi Kouki, Pigi Kuhlman, Christopher Kumar, Shachi Kuter, Ugur Lansky, Amy Lauw, Hady Lavedan, Christian Lavrac, N. Lerman, Kristina Licamele, Louis Liu, Xiangyang Liu, Huan Liu, Yan London, Ben Lu, Qing Machanavajjhala, Ashwin Mack, Kendra Macskassy, Sofus Mann, Janet Marathe, Achla Marcum, Christopher Mares, David Maulik, U. Mekaru, Sumiko Memory, Alex Miao, Hui Michelson, Matthew Mihalkova, Lilyana Milic-Frayling, Natasa Miller, Renee Minton, Steve Minton, Steven Mitkus, Shruti Moens, Marie-Francine Motoda, Hiroshi Mount, Stephen Moustafa, Walaa EldinMuggleton, Stephen Muthiah, Sathappan Namata, Galileo MarkNavlakha, Saket Nikolov, Nikola S.Norman, Joseph Norris, Eric Norton, Myra Nsoesie, Elaine Ntoulas, Alexcandros O'Leary, Dianne P.ODonovan, John Oard, Doug Onukwugha, Eberechukwu Ottosson, Gregor Papadimitriou, Panagiotis Pfeffer, Avi Piatetsky-Shapiro, Gregory Plangprasopchok, Anon Polymeropoulos, Mihales Polyzotis, Neoklis Pujara, Jay Pujara, Jay Pujra, Jay Ramakrishnan, Naren Ramesh, Arti Ramesh, Arti Rand, William Rao, Nikhil SRaschid, Louiqa Rastegari, Mohammad Rathod, Priyang Reddy, Charu C. AggarwaRekatsinas, Theodoros Rhee, Jeanne Riloff, Ellen Rodrigues, Eduarda MendesRodriguez, Mario Roussopoulos, Nick Saha, Barna Sahami, Mehran Saraf, Parang Sarawagi, Sunita Sayyadi, Hassan Schaffer, James Scheffer, Tobias Schmidler, Scott Schnaitter, Karl See, Kane Segal, Eran Sehgal, Vivek Self, Nathan Sen, Prithviraj Shahar, Yuval Sharara, Hossam Shashanka, Madhusudana Shen, Shitian Shneiderman, Ben Singh, Lisa Skomoroch, Peter Small, Peter Smith, Marc Somasundaran, Swapna Sopan, Awalin Springer, Aaron Sridhar, Dhanya Srinivasan, Aravind Srinivasan, Sriram Srivastava, Ashok Srivastava, Divesh Staats, Brian Subbaian, Karthik Subrahmanian, V. S.Summers, Kristin Tadepalli, Prasad Taskar, Benjamin Terzi, Evimaria Thompson, Spencer K.Ting, Hua Tomkins, Sabina Trinh, Khoa Tsaparas, Panayiotis Udrea, Octavian Viechnicki, Peter Volpi, Simona Vullikanti, Anil Walker, Marilyn Wang, Wei Weikum, Gerhard Welser, Howard Whittaker, Steve Wiebe, Janyce Wilbur, John Yeh, Tom Yoo, Jaebong Yu, Jiawei Han PhilipYu, Jun Zaki, Mohammed Zavorin, Ilya Zhang, Yi Zhang, Yue Zhao, Liang Zhao, Bin Zheleva, Elena desJardins, Marie

Type anyConference PaperJournal ArticleThesisTutorialBook ChapterUnpublishedPosterBook

Term any

Year any2019201820172016201520142013201220112010200920082007200620052004200320022001200019991998199719951994

Keyword anyComparative AnalysisDiscussion ForumsFirst-order probabilistic modelsHL-MRFsLDALifted inference and learningMOOCMOOC Discussion ForumsMOOCsModel ComparisonOnline CoursesPAC-BayesPar-factor graphsProbabilistic programmingSRLSeeded LDASocio-behavioral modelsStatistical relational learningTemplated graphical modelsUncertain GraphsVisualizing Uncertaintyanonymity onlinebioinformatics gene expression analysis antipsychotic pharmacogeneticscollective classificationdefectfeature generationfunctional biological signalsgene expression bioinformatics drug therapeuticsgeneralization boundsgroupshigh school MOOCsinfluencelatent variable modelslearner engagementlearning analyticslearning theoryonline educationprivacyprobabilistic modelingprobabilistic soft logicprofessional networkssearchsensitive attribute inferencesocial mediasocial networkssplice-sitestatistical relational languagestructured predictionstudent learningtutorialweb

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Zheleva, E. & Getoor, L. Preserving the Privacy of Sensitive Relationships in Graph Data. Proceedings of the First SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007) 4890, 153-171 (Springer, 2008).

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Zheleva, E. & Getoor, L. Preserving the Privacy of Sensitive Relationships in Graph Data. First ACM SIGKDD Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007) (2007).

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zheleva-pinkdd07.pdf (373.86 KB)

Zheleva, E. Prediction, Evolution and Privacy in Social and Affiliation Networks. (2011).

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zheleva-phdthesis11.pdf (5.81 MB)

Licamele, L. & Getoor, L. Predicting Protein-Protein Interactions Using Relational Features. ICML Workshop on Statistical Network Analysis (2006).

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Tomkins, S., Ramesh, A. & Getoor, L. Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study. International Conference on Educational Data Mining (EDM) (2016).

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tomkins-edm16.pdf (619.77 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).

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rastegari13.pdf (2.35 MB)

Lansky, A. & Getoor, L. Practical Planning in COLLAGE. Proceedings of the AAAI Fall Symposium on Planning and Learning: On to Real Applications (1994).

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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).

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sen-vldbj09.pdf (1.12 MB)

Namata, G. Mark & Getoor, L. A Pipeline Approach to Graph Identification. Seventh International Workshop on Mining and Learning with Graphs (2009).

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namatag-mlg09.pdf (93.77 KB)

Kouki, P., Schaffer, J., Pujara, J., ODonovan, J. & Getoor, L. Personalized Explanations for Hybrid Recommender Systems. Intelligent User Interfaces (2019).

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kouki-iui19.pdf (3.34 MB)

Elsayed, T., Oard, D., Namata, G. Mark & Getoor, L. Personal Name Resolution in Email: A Heuristic Approach. (2008).

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LAMP_150.pdf (397.61 KB)

Bach, S. H., Huang, B., Boyd-Graber, J. & Getoor, L. Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. International Conference on Machine Learning (ICML) (2015).

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bach-icml15.pdf (356.46 KB)

Hung, E., Getoor, L. & Subrahmanian, V. S. PXML: A Probabilistic Semistructured Data Model and Algebra. Proceedings of the IEEE International Conference on Data Engineering (2003).

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Getoor, L. & Grant, J. PRL: A Logical Approach to Probabilistic Relational Models. Machine Learning Journal 62, (2006).

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getoor-mlj06.pdf (685.04 KB)

London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayesian Collective Stability. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (2014).

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london-aistats14.pdf (490.14 KB)

London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayes Generalization Bounds for Randomized Structured Prediction. NIP Workshop on Perturbation, Optimization and Statistics (2013).

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london-nips13ws.pdf (205.57 KB)

O

Hwang, H., Lauw, H., Getoor, L. & Ntoulas, A. Organizing User Search Histories. IEEE Transactions on Knowledge and Data Engineering (2010).

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Somasundaran, S., Namata, G. Mark, Getoor, L. & Wiebe, J. Opinion Graphs for Polarity and Discourse Classification. TextGraphs-4: Graph-based Methods for Natural Language Processing (2009).

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somasundaran-textgraphs09.pdf (289.75 KB)

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Ontology-Aware Partitioning for Knowledge Graph Identification. CIKM Workshop on Automatic Knowledge Base Construction (2013).

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pujara_akbc13.pdf (370.62 KB)

Getoor, L. & Fromherz, M. Online Scheduling for Reprographic Machines. Working notes AAAI Workshop on Online Search (1997).

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Pujara, J., London, B., Getoor, L. & Cohen, W. Online Inference for Knowledge Graph Construction. Workshop on Statistical Relational AI (2015).

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pujara-starai15.pdf (340.95 KB)

Bhattacharya, I. & Getoor, L. Online Collective Entity Resolution. The 22nd National Conference on Artificial Intelligence (NECTAR Track) (AAAI Press, 2007).

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nectar07.pdf (395.24 KB)

N

Fakhraei, S., Huang, B., Raschid, L. & Getoor, L. Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2014).

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fakhraei-tcbb2014_accepted.pdf (3.97 MB)

Diehl, C., Getoor, L. & Namata, G. Mark. Name Reference Resolution in Organizational Email Archives. SIAM Conference on Data Mining (SDM) (2006).

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diehlsdm06.pdf (971.2 KB)

M

Ramesh, A., Rodriguez, M. & Getoor, L. Multi-relational influence models for online professional networks. International Conference on Web Intelligence (ICWI) 291-298 (ACM, 2017).

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ramesh-icwi17.pdf (761.17 KB)

London, B., Rekatsinas, T., Huang, B. & Getoor, L. Multi-relational Weighted Tensor Decomposition. NIPS Workshop on Spectral Learning (2012).

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london-nips12ws-mrwtd.pdf (326.3 KB)

London, B., Rekatsinas, T., Huang, B. & Getoor, L. Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss. (2013).

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mrwtd.pdf (460.45 KB)

Getoor, L. Multi-relational Data Mining Using Probabilistic Models. Multi-Relational Data Mining Workshop (2001).

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mrdm.pdf (109.57 KB)

Sharara, H., Halgin, D., Getoor, L. & Borgatti, S. Multi-dimensional Trajectory Analysis for Career Histories. International Sunbelt Social Networks Conference (Sunbelt XXXI) (2011).

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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).

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ramesh-nipsws13.pdf (153.92 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).

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saha-sdm08.pdf (233.12 KB)

Augustine, E. & Farnadi, G. MLTrain: Collective Reasoning With Probabilistic Soft Logic. (2018). at <https://github.com/linqs/psl-examples/tree/uai18>

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MLTrain - UAI 2018.pdf (8.93 MB)

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Rekatsinas, T., Deshpande, A. & Getoor, L. Local Structure and Determinism in Probabilistic Databases. ACM SIGMOD Conference (2012).

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sigmod_AAC2012.pdf (490.28 KB)

Lu, Q. & Getoor, L. Link-based Text Classification. IJCAI Workshop on "Text Mining and Link Analysis" (2003).

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ijcai03-ws.pdf (97.25 KB)

Lu, Q. & Getoor, L. Link-based Classification Using Labeled and Unlabeled Data. ICML Workshop on "The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining (2003).

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icml03-ws.pdf (274.65 KB)

Sen, P. & Getoor, L. Link-based Classification. (2007).

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senum-tr07.pdf (511.11 KB)

Getoor, L. Advanced Methods for Knowledge Discovery from Complex Data (Maulik, U., Holder, L. & Cook, D.) (Springer-Verlag, 2005).

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Lu, Q. & Getoor, L. Link-based Classification. Proceedings of the International Conference on Machine Learning (ICML) (2003).

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lu-icml03.pdf (195.81 KB)

Bilgic, M. & Getoor, L. Link-based Active Learning. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).

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mbilgic-nips09wkshp.pdf (116.35 KB)

Namata, G. Mark & Getoor, L. Link Prediction. Encyclopedia of Machine Learning (2010).

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Getoor, L. & Diehl, C. Link Mining: A Survey. SigKDD Explorations Special Issue on Link Mining 7, (2005).

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Getoor, L. Link Mining: A New Data Mining Challenge. SIGKDD Explorations, volume 5, 85- -89 (2003).

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Kimmig, A., Mihalkova, L. & Getoor, L. Lifted graphical models: a survey. Machine Learning 1-45 (2014).

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Kimmig, A., Mihalkova, L. & Getoor, L. Lifted graphical models: a survey. Machine Learning Journal 99, 1–45 (2015).

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kimmig-mlj15.pdf (785.58 KB)

Srinivasan, S., Babaki, B., Farnadi, G. & Getoor, L. Lifted Hinge-Loss Markov Random Fields. 33rd AAAI Conference on Artificial Intelligence (2019).

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srinivasan-aaai19.pdf (417.5 KB)

Mihalkova, L. & Getoor, L. Lifted Graphical Models: A Survey. (2011).

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1107.4966v2.pdf (446.54 KB)

Smith, M., Barash, V., Getoor, L. & Lauw, H. Leveraging Social Context for Searching Social Media. CIKM Workshop on Search in Social Media (2008).

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Udrea, O., Getoor, L. & Miller, R. Leveraging Data and Structure in Ontology Integration. Proceedings of ACM-SIGMOD 2007 International Conference on Management 449–460 (2007).

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p449.pdf (509.48 KB)

Mihalkova, L., Moustafa, W. Eldin & Getoor, L. Learning to Predict Web Collaborations. WSDM Workshop on User Modeling for Web Applications (2011).

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mihalkova-wikiCollabs.pdf (353.9 KB)

Getoor, L., Friedman, N. & Koller, D. Learning Structured Statistical Models from Relational Data. Electronic Transactions on Artificial Intelligence 6, (2002).

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