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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 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 Memory, Alex Miao, Hui Michelson, Matthew Mihalkova, Lilyana Milic-Frayling, Natasa Miller, Renee Miller, Renee JMinton, 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

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Year any2019201820172016201520142013201220112010200920082007200620052004200320022001200019991998199719951994

Keyword anyCognitionComparative AnalysisComplexity theoryData engineeringDiscussion ForumsFirst-order probabilistic modelsHL-MRFsKnowledge engineeringLDALifted inference and learningMOOCMOOC Discussion ForumsMOOCsMetadataModel ComparisonOnline CoursesPAC-BayesPar-factor graphsProbabilistic logicProbabilistic programmingSRLSchema mappingSeeded LDASocio-behavioral modelsStatistical relational learningTask analysisTemplated graphical modelsUncertain GraphsVisualizing Uncertaintyanonymity onlinebioinformatics gene expression analysis antipsychotic pharmacogeneticscollective classificationcollective mapping discoverydata integrationdefectfeature generationfunctional biological signalsgene expression bioinformatics drug therapeuticsgeneralization boundsgroupshigh school MOOCsinference mechanismsinfluencelatent variable modelslearner engagementlearning analyticslearning theorymeta dataonline educationoptimisationoptimizationpotential mappingsprivacyprobabilistic modelingprobabilistic reasoning techniquesprobabilistic soft logicprobabilityprofessional networksschema mapping optimization problemsearchsensitive attribute inferencesocial mediasocial networkssplice-sitestatistical relational languagestructured predictionstudent learningtutorialuncertainty handlingweb

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Broecheler, M. & Getoor, L. Computing marginal distributions over continuous Markov networks for statistical relational learning. Advances in Neural Information Processing Systems (NIPS) (2010).

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broecheler-nips10.pdf (382.51 KB)

Broecheler, M., Mihalkova, L. & Getoor, L. Probabilistic Similarity Logic. Conference on Uncertainty in Artificial Intelligence (2010).

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broecheler-uai10.pdf (399.54 KB)

Broecheler, M. & Getoor, L. Probabilistic Similarity Logic. International Workshop on Statistical Relational Learning (SRL'09) (2009).

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broecheler-srl09.pdf (176.13 KB)

Bradley, S. & Getoor, L. Topic Modeling for Wikipedia Link Disambiguation. ACM Transactions on Information Systems 32, (2014).

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Bilgic, M. & Getoor, L. Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition. Journal of Artificial Intelligence Research (JAIR) 41, 69–95 (2011).

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bilgic11a.pdf (1.64 MB)

Bilgic, M. & Getoor, L. Active Inference for Collective Classification. Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track) 1652–1655 (2010).

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bilgic-aaai10.pdf (387.53 KB)

Bilgic, M., Mihalkova, L. & Getoor, L. Active Learning for Networked Data. Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010).

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bilgic-icml10.pdf (515.65 KB)

Bilgic, M. Information Acquisition in Structured Domains. (2010).

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mbilgic-phdthesis.pdf (4.68 MB)

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)

Bilgic, M. & Getoor, L. Reflect and Correct: A Misclassification Prediction Approach to Active Inference. ACM Transactions on Knowledge Discovery from Data 3, 1–32 (2009).

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bilgic-tkdd09.pdf (3.66 MB)

Bilgic, M. & Getoor, L. Effective Label Acquisition for Collective Classification. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 43–51 (2008).

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bilgic-kdd08.pdf (758.14 KB)

Bilgic, M., Namata, G. Mark & Getoor, L. Combining Collective Classification and Link Prediction. Workshop on Mining Graphs and Complex Structures at the IEEE International Conference on Data Mining (ICDM-2007) (2007).

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mgcs07.pdf (105.13 KB)

Bilgic, M. & Getoor, L. VOILA: Efficient Feature-value Acquisition for Classification. AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence (2007).

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bilgic-aaai07.pdf (220.47 KB)

Bilgic, M., Licamele, L., Getoor, L. & Shneiderman, B. D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. Visual Analytics Science and Technology (VAST) (2006).

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Bilgic, M., Licamele, L., Getoor, L. & Shneiderman, B. D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. International Symposium on Graph Drawing (Healy, P. & Nikolov, N. S.) 3843, 505–507 (Springer, 2005).

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ddupe.pdf (224.93 KB)

Bhattacharya, I. & Getoor, L. CRC Data Mining Series 223-243 (Chapman and Hall, 2008).

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Bhattacharya, I. & Getoor, L. Collective Entity Resolution In Relational Data. ACM Transactions on Knowledge Discovery from Data 1, 1-36 (2007).

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bhattacharya-tkdd.pdf (346.13 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)

Bhattacharya, I. & Getoor, L. Query-time Entity Resolution. Journal of Artificial Intelligence Research (JAIR) 30, 621–657 (2007).

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bhattacharya07a.pdf (309.63 KB)

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)

Bhattacharya, I. Collective Entity Resolution In Relational Data. (2006).

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thesis.pdf (761.21 KB)

Bhattacharya, I. & Getoor, L. Collective Entity Resolution in Relational Data. Data Engineering Bulletin 29, (2006).

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Bhattacharya, I. & Getoor, L. Entity Resolution in Social Networks. International Sunbelt Social Network Conference (Sunbelt XXVI) (2006).

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Bhattacharya, I. & Getoor, L. Mining Graph Data (Cook, D. & Holder, L.) (Wiley, 2006).

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Bhattacharya, I., Licamele, L. & Getoor, L. Query-Time Entity Resolution. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006).

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kdd06.pdf (183.45 KB)

Bhattacharya, I., Licamele, L. & Getoor, L. Relational Clustering for Entity Resolution Queries. ICML Workshop on Statistical Relational Learning (SRL) (2006).

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bhattacharyaicml06-wkshp.pdf (195.79 KB)

Bhattacharya, I. & Getoor, L. Relational Clustering for Multi-type Entity Resolution. ACM SIGKDD Workshop on Multi Relational Data Mining (MRDM) (2005).

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bhattacharyakdd05-whskp.pdf (259.82 KB)

Bhattacharya, I. & Getoor, L. Deduplication and Group Detection using Links. ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD) (2004).

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bhattacharyakdd04-whskp.pdf (231.67 KB)

Bhattacharya, I. & Getoor, L. Iterative Record Linkage for Cleaning and Integration. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD) (2004).

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bhattacharyasigmod04-wkshp.pdf (222.38 KB)

Bhattacharya, I., Getoor, L. & Bengio, Y. Unsupervised Sense Disambiguation using Bilingual Probabilistic Models. Annual Meeting of the Association for Computational Linguistics (ACL) (2004).

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acl04.pdf (156.26 KB)

Barash, V., Smith, M., Getoor, L. & Welser, H. Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context. International Conference on Weblogs and Social Media (2009).

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barash-icwsm09.pdf (171.16 KB)

Bach, S. H., Huang, B. & Getoor, L. Probabilistic Soft Logic for Social Good. KDD Workshop on Data Science for Social Good (2014).

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bach-dssg14.pdf (124.88 KB)

Bach, S. H., Huang, B., London, B. & Getoor, L. Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. Uncertainty in Artificial Intelligence (2013).

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bach-uai13.pdf (379.45 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)

Bach, S. H., Broecheler, M., Getoor, L. & O'Leary, D. P. Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization. Advances in Neural Information Processing Systems (NIPS) 2663–2671 (2012).

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bach-nips12.pdf (274.58 KB)

Bach, S. H., Broecheler, M., Kok, S. & Getoor, L. Decision-Driven Models with Probabilistic Soft Logic. NIPS Workshop on Predictive Models in Personalized Medicine (2010).

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bach-pmpm10.pdf (246.79 KB)

Bach, S. H., Broecheler, M., Huang, B. & Getoor, L. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. Journal of Machine Learning Research (JMLR) 18, 1-67 (2017).

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bach-jmlr17.pdf (731.56 KB)

Bach, S. H., Broecheler, M., Huang, B. & Getoor, L. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. ArXiv:1505.04406 [cs.LG] (2015).

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bach-arxiv15.pdf (686.27 KB)

Bach, S. H. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction. (2015).

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bach-thesis15.pdf (1.17 MB)

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)

Bach, S. H., Huang, B. & Getoor, L. Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. Artificial Intelligence and Statistics (AISTATS) (2015).

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bach-aistats15.pdf (345.2 KB)

Bach, S. H., Huang, B. & Getoor, L. Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML) (2014).

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bach-discml14.pdf (254.9 KB)

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