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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 Raschid, 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 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 anyJournal ArticleConference PaperThesisTutorialBook 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 pharmacogeneticsfeature generationfunctional biological signalsgene expression bioinformatics drug therapeuticsgeneralization boundsgroupshigh school MOOCsinfluencelatent variable modelslearner engagementlearning analyticslearning theoryonline educationprivacyprobabilistic modelingprofessional networkssensitive attribute inferencesocial mediasocial networkssplice-sitestructured predictionstudent learningtutorialweb

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2019

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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LHL-MRF_camera_ready.pdf (417.5 KB)

2015

Foulds, J., Kumar, S. & Getoor, L. Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models. International Conference on Machine Learning (ICML) (2015).

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Foulds2015LatentTopicNetworks.pdf (382.53 KB)

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)

2014

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).

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ramesh-aaai14.pdf (505.47 KB)

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

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2013

Kang, J., Lerman, K. & Getoor, L. LA-LDA: A Limited Attention Topic Model for Social Recommendation. The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013) (2013).

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kang-sbp13.pdf (622.52 KB)

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Large-Scale Knowledge Graph Identification using PSL. ICML Workshop on Structured Learning (SLG) (2013).

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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).

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pujara_s4bd13.pdf (306.96 KB)

Bach, S. H., Huang, B. & Getoor, L. Large-margin Structured Learning for Link Ranking. NIPS Workshop on Frontiers of Network Analysis: Methods, Models, and Applications (2013).

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bach-fna13.pdf (210.09 KB)

Bach, S. H., Huang, B. & Getoor, L. Learning Latent Groups with Hinge-loss Markov Random Fields. ICML Workshop on Inferning: Interactions between Inference and Learning (2013).

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bach-inferning13.pdf (348.79 KB)

2012

Pujara, J. & Skomoroch, P. Large-Scale Hierarchical Topic Models. NIPS Workshop on Big Learning (2012).

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pujara_biglearn12.pdf (189.96 KB)

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)

2011

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)

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

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1107.4966v2.pdf (446.54 KB)

2010

Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Learning Algorithms for Link Prediction based on Chance Constraints. European Conference on Machine Learning (ECML) (2010).

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doppa-ecml10.pdf (203 KB)

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

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2009

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)

2008

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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2007

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)

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

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senum-tr07.pdf (511.11 KB)

2006

Bhattacharya, I. & Getoor, L. A Latent Dirichlet Model for Unsupervised Entity Resolution. SIAM Conference on Data Mining (SDM) (2006).

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bhattacharyasdm06.pdf (209.24 KB)

2005

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

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

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2003

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

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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)

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)

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)

2002

Getoor, L., Friedman, N., Koller, D. & Taskar, B. Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research 3, 679- -707 (2002).

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jmlr02.pdf (502.22 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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2001

Getoor, L., Friedman, N., Koller, D. & Taskar, B. Learning Probabilistic Models of Relational Structure. Proceedings of International Conference on Machine Learning (ICML) (2001).

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icml01.pdf (157.91 KB)

Getoor, L., Friedman, N., Koller, D. & Pfeffer, A. Relational Data Mining (Dzeroski, S. & Lavrac, N.) (Springer-Verlag, 2001).

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Getoor, L., Friedman, N., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. Relational Data Mining (Springer-Verlag, 2001).

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lprm-ch.pdf (376 KB)

Getoor, L. Learning Statistical Models from Relational Data. (2001).

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getoor-thesis.pdf (3.39 MB)

2000

Getoor, L., Koller, D., Taskar, B. & Friedman, N. Learning Probabilistic Relational Models with Structural Uncertainty. Proceedings of the AAAI Workshop on Learning Statistical Models from Relational Data (2000).

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1999

Friedman, N., Getoor, L., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. International Joint Conference on Articial Intelligence (1999).

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icjai99.pdf (156.94 KB)

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