Publications

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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).PDF icon embar-akbc19.pdf (47.25 KB)
Sridhar, D. & Getoor, L. Estimating Causal Effects of Tone in Online Debates. International Joint Conference on Artificial Intelligence (2019).PDF icon sridhar-ijcai19.pdf (220.49 KB)
Srinivasan, S., Rao, N. S., Subbaian, K. & Getoor, L. Identifying Facet Mismatches In Search Via Micrographs. The 28th ACM International Conference on Information and Knowledge Management (2019).
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).PDF icon 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).PDF icon 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).PDF icon kouki-iui19.pdf (3.34 MB)
Embar, V., Srinivasan, S. & Getoor, L. Tractable Marginal Inference for Hinge-Loss Markov Random Fields. Third ICML workshop on Tractable Probabilistic Modeling (2019).PDF icon embar-icmlws19.pdf (410.24 KB)
Augustine, E., Rekatsinas, T. & Getoor, L. Tractable Probabilistic Reasoning Through Effective Grounding. Third ICML workshop on Tractable Probabilistic Modeling (2019).PDF icon augustine-tpm19.pdf (224.8 KB)
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).PDF icon embar-kbcom18.pdf (577.65 KB)
Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018).PDF icon 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).PDF icon 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>PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon icdm_2018.pdf (473.58 KB)
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/>PDF icon 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).PDF icon 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).PDF icon 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>PDF icon 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).PDF icon 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).
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>PDF icon 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>PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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>PDF icon 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).PDF icon 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).PDF icon 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).PDF icon fakhraei_mlg_2016.pdf (711.26 KB)
Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Magazine 37, 63–75 (2016).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon sridhar-bioinformatics_2016.pdf (1.94 MB)
Sridhar, D. & Getoor, L. Probabilistic Inference for Causal Structure Discovery. Uncertainty in Artificial Intelligence (UAI) Workshop on Causation (2016).PDF icon sridhar-uai-open-problem-v2.pdf (118.31 KB)
Rekatsinas, T., Deshpande, A., Dong, X. Luna, Getoor, L. & Srivastava, D. SourceSight: Enabling Effective Source Selection. ACM SIGMOD Conference (2016).PDF icon modde087.pdf (799.94 KB)
London, B., Huang, B. & Getoor, L. Stability and Generalization in Structured Prediction. Journal of Machine Learning Research 17, (2016).PDF icon 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).PDF icon 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).PDF icon london-icml15.pdf (788.06 KB)
Pujara, J., London, B. & Getoor, L. Budgeted Online Collective Inference. Uncertainty in Artificial Intelligence (2015).PDF icon pujara-uai15.pdf (302.63 KB)
Namata, G. Mark, London, B. & Getoor, L. Collective Graph Identification. ACM Transactions on Knowledge Discovery from Data 10, 25:1–25:36 (2015).PDF icon namata-tkdd.pdf (500.96 KB)

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