Publications

Export 319 results:
Author Title [ Year(Asc)]
2019
A. Kimmig, Memory, A., Miller, R. J., and Getoor, L., A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence, IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 31, no. 8, p. 1426--1439, 2019.PDF icon kimming-tkde19.pdf (713.64 KB)
G. Farnadi, Babaki, B., and Getoor, L., A Declarative Approach to Fairness in Relational Domains, IEEE Data Engineering Bulletin, vol. 42, no. 3, p. 36--48, 2019.PDF icon farnadi-de19.pdf (365.14 KB)
V. Embar, Pujara, J., and Getoor, L., Collective Alignment of Large-scale Ontologies, in AKBC Workshop on Federated Knowledge Bases (FKBs), 2019.PDF icon embar-fkbs19.pdf (57.36 KB)
D. Sridhar and Getoor, L., Estimating Causal Effects of Tone in Online Debates, in International Joint Conference on Artificial Intelligence (IJCAI), 2019.PDF icon sridhar-ijcai19.pdf (220.49 KB)
S. Srinivasan, Rao, N. S., Subbaian, K., and Getoor, L., Identifying Facet Mismatches In Search Via Micrographs, in International Conference on Information and Knowledge Management (CIKM), 2019.PDF icon srinivasan-cikm19.pdf (887.06 KB)
A. Ramesh, Goldwasser, D., Huang, B., Daume, III, H., and Getoor, L., Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields, IEEE Transactions on Learning Technologies (TLT), vol. 14, pp. 1-1, 2019.PDF icon ramesh-tlt19.pdf (4.3 MB)
S. Srinivasan, Babaki, B., Farnadi, G., and Getoor, L., Lifted Hinge-Loss Markov Random Fields, in AAAI Conference on Artificial Intelligence (AAAI), 2019.PDF icon srinivasan-aaai19.pdf (417.5 KB)
P. Kouki, Schaffer, J., Pujara, J., Odonovan, J., and Getoor, L., Personalized Explanations for Hybrid Recommender Systems, in Intelligent User Interfaces (IUI), 2019.PDF icon kouki-iui19.pdf (3.34 MB)
V. Embar, Srinivasan, S., and Getoor, L., Tractable Marginal Inference for Hinge-Loss Markov Random Fields, in ICML Workshop on Tractable Probabilistic Modeling (TPM), 2019.
E. Augustine, Rekatsinas, T., and Getoor, L., Tractable Probabilistic Reasoning Through Effective Grounding, in ICML Workshop on Tractable Probabilistic Modeling (TPM), 2019.PDF icon augustine-tpm19.pdf (224.8 KB)
S. Tomkins and Getoor, L., Understanding Hybrid-MOOC Effectiveness with a Collective Socio-Behavioral Model, Journal of Educational Data Mining (JEDM), vol. 11, p. 42--77, 2019.PDF icon tomkins-jedm19.pdf (679.09 KB)
2018
E. Augustine and Getoor, L., A Comparison of Bottom-Up Approaches to Grounding for Templated Markov Random Fields, in Machine Learning and Systems (MLSys), 2018.PDF icon augustine-sysml18.pdf (624.33 KB)
G. Farnadi, Kouki, P., Thompson, S. K., Srinivasan, S., and Getoor, L., A Fairness-aware Hybrid Recommender System, in RecSys Workshop on Responsible Recommendation (FATREC), 2018.PDF icon farnadi-fatrec18.pdf (474.04 KB)
S. Tomkins, Getoor, L., Chen, Y., and Zhang, Y., A Socio-linguistic Model for Cyberbullying Detection, in International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018.PDF icon tomkins-asonam18.pdf (299.34 KB)
Y. Zhang, Ramesh, A., Golbeck, J., Sridhar, D., and Getoor, L., A Structured Approach to Understanding Recovery and Relapse in AA, in The Web Conference (WWW), 2018.PDF icon zhang-www18.pdf (800.66 KB)
V. Embar, Farnadi, G., Pujara, J., and Getoor, L., Aligning Product Categories using Anchor Products, in Workshop on Knowledge Base Construction, Reasoning and Mining (KBCOM), 2018.PDF icon embar-kbcom18.pdf (577.65 KB)
J. Chang, Chen, R., Pujara, J., and Getoor, L., Clustering System Data using Aggregate Measures, in Machine Learning and Systems (MLSys), 2018.PDF icon chang-sysml18.pdf (299.32 KB)
P. Kouki, Pujara, J., Marcum, C., Koehly, L., and Getoor, L., Collective Entity Resolution in Multi-Relational Familial Networks, Knowledge and Information Systems (KAIS), vol. 61, no. 3, p. 1547-–1581, 2018.PDF icon kouki-kais18.pdf (1.17 MB)
D. Sridhar, Springer, A., Hollis, V., Whittaker, S., and Getoor, L., Estimating Causal Effects of Exercise from Mood Logging Data, in ICML Workshop on Causal Machine Learning (CausalML), 2018.PDF icon sridhar-causalml18.pdf (333.69 KB)
G. Farnadi, Babaki, B., and Getoor, L., Fairness in Relational Domains, in Artificial Intelligence, Ethics, and Society (AIES), 2018.PDF icon farnadi_aies2018.pdf (418.24 KB)
G. Farnadi, Babaki, B., and Getoor, L., Fairness-aware Relational Learning and Inference, in AAAI Workshop on Declarative Learning Based Programming (DeLBP), 2018.PDF icon farnadi-delbp2018.pdf (169.37 KB)
E. Augustine and Farnadi, G., MLTrain: Collective Reasoning With Probabilistic Soft Logic. Uncertainty in Artificial Intelligence (UAI), 2018.PDF icon augustine-uai18.pdf (8.93 MB)
P. Kouki, Resolution, Recommendation, and Explanation in Richly Structured Social Networks, 2018.PDF icon kouki-dissertation.pdf (6.94 MB)
D. Sridhar, Pujara, J., and Getoor, L., Scalable Probabilistic Causal Structure Discovery, in International Joint Conference on Artificial Intelligence (IJCAI), 2018.PDF icon sridhar-ijcai18.pdf (281.32 KB)
V. Embar, Sridhar, D., Farnadi, G., and Getoor, L., Scalable Structure Learning for Probabilistic Soft Logic, in IJCAI Workshop on Statistical Relational AI (StarAI), 2018.PDF icon embar-starai18.pdf (400.23 KB)
S. Tomkins, Isley, S., London, B., and Getoor, L., Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations, in Recommender Systems (RecSys), 2018.PDF icon tomkins-recsys18.pdf (655.92 KB)
S. Tomkins, Farnadi, G., Amantullah, B., Getoor, L., and Minton, S., The Impact of Environmental Stressors on Human Trafficking, in ICWSM Workshop on Beyond Online Data (BOD), 2018.PDF icon tomkins-bod18.pdf (473.58 KB)
S. Tomkins, Farnadi, G., Amantullah, B., Getoor, L., and Minton, S., The Impact of Environmental Stressors on Human Trafficking, in International Conference on Data Mining (ICDM), 2018.PDF icon tomkins-icdm18.pdf (473.58 KB)
A. Ramesh and Getoor, L., Understanding Evolution of Long-running MOOCs, in International Conference on Web Information Systems Engineering (WISE), 2018.
2017
P. Kouki, Pujara, J., Marcum, C., Koehly, L., and Getoor, L., Collective Entity Resolution in Familial Networks, in IEEE International Conference on Data Mining (ICDM), 2017.PDF icon kouki-icdm17.pdf (653.4 KB)
A. Kimmig, Memory, A., Miller, R., and Getoor, L., A Collective, Probabilistic Approach to Schema Mapping, in International Conference on Data Engineering (ICDE), 2017.PDF icon kimmig-icde17.pdf (463.69 KB)
S. Tomkins, Getoor, L., Chen, Y., and Zhang, Y., Detecting Cyber-bullying from Sparse Data and Inconsistent Labels, in Learning with Limited Labeled Data (LLD) NIPS Workshop, 2017.PDF icon tomkins-NIPSLLD17.pdf (286.95 KB)
S. Tomkins, Pujara, J., and Getoor, L., Disambiguating Energy Disaggregation: A Collective Probabilistic Approach, in International Joint Conference on Artifi cial Intelligence, 2017.PDF icon tomkins-ijcai17.pdf (373.28 KB)
S. H. Bach, Broecheler, M., Huang, B., and Getoor, L., Hinge-Loss Markov Random Fields and Probabilistic Soft Logic, Journal of Machine Learning Research (JMLR), vol. 18, pp. 1-67, 2017.PDF icon bach-jmlr17.pdf (731.56 KB)
A. Ramesh, Rodriguez, M., and Getoor, L., Multi-relational influence models for online professional networks, in International Conference on Web Intelligence (ICWI), 2017, pp. 291-298.PDF icon ramesh-icwi17.pdf (761.17 KB)
S. Kim, Kini, N., Pujara, J., Koh, E., and Getoor, L., Probabilistic Visitor Stitching on Cross-Device Web Logs, in International Conference on World Wide Web (WWW), 2017, pp. 1581–1589.PDF icon p1581-kimwww17.pdf (1.23 MB)
G. Farnadi, Bach, S. H., Moens, M. - F., Getoor, L., and De Cock, M., Soft quantification in statistical relational learning, Machine Learning Journal, 2017.PDF icon farnadi-mlj17.pdf (1.24 MB)
J. Pujara, Augustine, E., and Getoor, L., Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short, in Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.PDF icon pujara-emnlp17.pdf (677.74 KB)
P. Kouki, Schaffer, J., Pujara, J., ODonovan, J., and Getoor, L., User Preferences for Hybrid Explanations, in 11th ACM Conference on Recommender Systems (RecSys), 2017.PDF icon kouki-recsys17.pdf (2.64 MB)
D. Sridhar, Pujara, J., and Getoor, L., Using Noisy Extractions to Discover Causal Knowledge, in NIPS Workshop on Automated Knowledge Base Construction, 2017.PDF icon sridhar-akbc17.pdf (203.34 KB)

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