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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 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 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

Embar, V., Pujara, J. & Getoor, L. Collective Alignment of Large-scale Ontologies. AKBC Workshop on Federated KBs and the Open Knowledge Network (2019).

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embar-akbc19.pdf (47.25 KB)

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).

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ramesh-tlt19.pdf (4.3 MB)

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)

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)

Augustine, E., Rekatsinas, T. & Getoor, L. Tractable Probabilistic Reasoning Through Effective Grounding. ICML TPM (2019).

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2018

Embar, V., Farnadi, G., Pujara, J. & Getoor, L. Aligning Product Categories using Anchor Products. First Workshop on Knowledge Base Construction, Reasoning and Mining (2018).

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embar-kbcom18.pdf (577.65 KB)

Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018).

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chang-sysml18.pdf (299.32 KB)

Kouki, P., Pujra, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Multi-Relational Familial Networks. Knowledge and Information Systems (KAIS) (2018).

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kouki-kais18.pdf (1.17 MB)

Augustine, E. & Getoor, L. A Comparison of Bottom-Up Approaches to Grounding for Templated Markov Random Fields. SysML (2018). at <https://github.com/eriq-augustine/grounding-experiments>

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augustine-sysml18.pdf (624.33 KB)

Sridhar, D., Springer, A., Hollis, V., Whittaker, S. & Getoor, L. Estimating Causal Effects of Exercise from Mood Logging Data. ICML Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (2018).

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sridhar-causalml18.pdf (333.69 KB)

Farnadi, G., Babaki, B. & Getoor, L. Fairness in Relational Domains. AAAI/ACM Conference on AI, Ethics, and Society (2018).

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farnadi_aies2018.pdf (418.24 KB)

Farnadi, G., Kouki, P., Thompson, S. K., Srinivasan, S. & Getoor, L. A Fairness-aware Hybrid Recommender System. The 2nd FATREC Workshop on Responsible Recommendation (2018).

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GFarnadi-FATREC2018.pdf (474.04 KB)

Farnadi, G., Babaki, B. & Getoor, L. Fairness-aware Relational Learning and Inference. Third International Workshop on Declarative Learning Based Programming (DeLBP) at thirty-second AAAI conference on Artificial Intelligence (2018).

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GFarnadi-DeLBP2018.pdf (169.37 KB)

Tomkins, S., Farnadi, G., Amantullah, B., Getoor, L. & Minton, S. The Impact of Environmental Stressors on Human Trafficking. Beyond Online Data (ICWSM Workshop) (2018).

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icdm_2018.pdf (473.58 KB)

Tomkins, S., Farnadi, G., Amantullah, B., Getoor, L. & Minton, S. The Impact of Environmental Stressors on Human Trafficking. International Conference on Data Mining (ICDM) (2018).

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icdm_2018.pdf (473.58 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)

Kouki, P. Resolution, Recommendation, and Explanation in Richly Structured Social Networks. Technology and Information Management Ph.D. Thesis, (2018).

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kouki-dissertation.pdf (6.94 MB)

Sridhar, D., Pujara, J. & Getoor, L. Scalable Probabilistic Causal Structure Discovery. International Joint Conference on Artificial Intelligence (2018). at <https://bitbucket.org/linqs/causpsl/src/master/>

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sridhar-ijcai18.pdf (281.32 KB)

Embar, V., Sridhar, D., Farnadi, G. & Getoor, L. Scalable Structure Learning for Probabilistic Soft Logic. Workshop on Statistical Relational AI (2018).

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VEmbar-StarAI2018.pdf (400.23 KB)

Tomkins, S., Getoor, L., Chen, Y. & Zhang, Y. A Socio-linguistic Model for Cyberbullying Detection. International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018).

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tomkins-asonam18.pdf (299.34 KB)

Zhang, Y., Ramesh, A., Golbeck, J., Sridhar, D. & Getoor, L. A Structured Approach to Understanding Recovery and Relapse in AA. The Web Conference (WWW) (2018). at <https://github.com/yzhan202/zhang-www18-experiments>

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zhang-www18.pdf (800.66 KB)

Tomkins, S., Isley, S., London, B. & Getoor, L. Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations. Recommender Systems (RecSys) (2018).

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recsys_2018.pdf (655.92 KB)

Ramesh, A. & Getoor, L. Understanding Evolution of Long-running MOOCs. International Conference on Web Information Systems Engineering (WISE) (2018).

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2017

Kouki, P., Pujara, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Familial Networks. IEEE International Conference on Data Mining (ICDM) (2017). at <https://github.com/pkouki/icdm2017>

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kouki-icdm17.pdf (653.4 KB)

Kimmig, A., Memory, A., Miller, R. & Getoor, L. A Collective, Probabilistic Approach to Schema Mapping. International Conference on Data Engineering (ICDE) (2017). at <https://github.com/alexmemory/kimmig-icde17/wiki>

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kimmig-icde17.pdf (463.69 KB)

Tomkins, S., Getoor, L., Chen, Y. & Zhang, Y. Detecting Cyber-bullying from Sparse Data and Inconsistent Labels. Learning with Limited Labeled Data (LLD) NIPS Workshop (2017).

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tomkins-NIPSLLD17.pdf (286.95 KB)

Tomkins, S., Pujara, J. & Getoor, L. Disambiguating Energy Disaggregation: A Collective Probabilistic Approach. International Joint Conference on Artificial Intelligence (2017).

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tomkins-ijcai17.pdf (373.28 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)

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)

Kim, S., Kini, N., Pujara, J., Koh, E. & Getoor, L. Probabilistic Visitor Stitching on Cross-Device Web Logs. International Conference on World Wide Web (WWW) 1581–1589 (2017).

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p1581-kimwww17.pdf (1.23 MB)

Farnadi, G., Bach, S. H., Moens, M. - F., Getoor, L. & De Cock, M. Soft quantification in statistical relational learning. Machine Learning Journal (2017).

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farnadi-mlj17.pdf (1.24 MB)

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>

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pujara-emnlp17.pdf (677.74 KB)

Kouki, P., Schaffer, J., Pujara, J., ODonovan, J. & Getoor, L. User Preferences for Hybrid Explanations. 11th ACM Conference on Recommender Systems (RecSys) (2017).

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kouki-recsys17.pdf (2.64 MB)

Sridhar, D., Pujara, J. & Getoor, L. Using Noisy Extractions to Discover Causal Knowledge. NIPS Workshop on Automated Knowledge Base Construction (2017).

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sridhar-akbc17.pdf (203.34 KB)

2016

Fakhraei, S., Sridhar, D., Pujara, J. & Getoor, L. Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. 12th International SIGKDD Workshop on Mining and Learning with Graphs (MLG) (ACM SIGKDD, 2016).

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fakhraei_mlg_2016.pdf (711.26 KB)

Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Magazine 37, 63–75 (2016).

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muthiah-aimag16.pdf (1.23 MB)

Kouki, P., Marcum, C., Koehly, L. & Getoor, L. Entity Resolution in Familial Networks. 12th International Workshop on Mining and Learning with Graphs (2016).

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kouki-mlg16.pdf (633.08 KB)

Rekatsinas, T. et al. Forecasting Rare Disease Outbreaks from Open Source Indicators. Statistical Analysis and Data Mining: The ASA Data Science Journal (2016).

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rekatsinas-sadm17.pdf (303.08 KB)

Pujara, J. & Getoor, L. Generic Statistical Relational Entity Resolution in Knowledge Graphs. Sixth International Workshop on Statistical Relational AI (IJCAI 2016, 2016).

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pujara-starai16.pdf (151.37 KB)

Sridhar, D. & Getoor, L. Joint Probabilistic Inference of Causal Structure. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop on Causal Discovery (2016).

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sridhar-kdd-ws16.pdf (204.51 KB)

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)

Sridhar, D., Fakhraei, S. & Getoor, L. A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction. Bioinformatics (2016).

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sridhar-bioinformatics_2016.pdf (1.94 MB)

Ramesh, A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. (2016).

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ramesh-thesis16.pdf (865.41 KB)

Sridhar, D. & Getoor, L. Probabilistic Inference for Causal Structure Discovery. Uncertainty in Artificial Intelligence (UAI) Workshop on Causation (2016).

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sridhar-uai-open-problem-v2.pdf (118.31 KB)

Pujara, J. Probabilistic Models for Scalable Knowledge Graph Construction. (2016).

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pujara-thesis15.pdf (1.06 MB)

Rekatsinas, T., Deshpande, A., Dong, X. Luna, Getoor, L. & Srivastava, D. SourceSight: Enabling Effective Source Selection. ACM SIGMOD Conference (2016).

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modde087.pdf (799.94 KB)

London, B., Huang, B. & Getoor, L. Stability and Generalization in Structured Prediction. Journal of Machine Learning Research 17, (2016).

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london-jlmr17.pdf (532.8 KB)

Kumar, S. et al. Unsupervised Models for Predicting Strategic Relations between Organizations. IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE, 2016).

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kumar_asonam16.pdf (212.61 KB)

2015

London, B., Huang, B. & Getoor, L. The Benefits of Learning with Strongly Convex Approximate Inference. International Conference on Machine Learning (ICML) (2015).

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london-icml15.pdf (788.06 KB)

Pujara, J., London, B. & Getoor, L. Budgeted Online Collective Inference. Uncertainty in Artificial Intelligence (2015).

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pujara-uai15.pdf (302.63 KB)

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