Publications

Export 300 results:
Author Keyword Title Type [ Year(Asc)]
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). (577.65 KB)
Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018). (299.32 KB)
Kouki, P., Pujara, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Multi-Relational Familial Networks. Knowledge and Information Systems (KAIS) (2018). at <https://github.com/pkouki/icdm2017> (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> (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). (333.69 KB)
Farnadi, G., Babaki, B. & Getoor, L. Fairness in Relational Domains. AAAI/ACM Conference on AI, Ethics, and Society (2018). (418.24 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).
Tomkins, S., Farnadi, G., Amantullah, B., Getoor, L. & Minton, S. The Impact of Environmental Stressors on Human Trafficking. Beyond Online Data (ICWSM Workshop) (2018).
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/> (281.32 KB)
Embar, V., Sridhar, D., Farnadi, G. & Getoor, L. Scalable Structure Learning for Probabilistic Soft Logic. Workshop on Statistical Relational AI (2018).
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).
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> (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).
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> (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> (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). (286.95 KB)
Tomkins, S., Pujara, J. & Getoor, L. Disambiguating Energy Disaggregation: A Collective Probabilistic Approach. International Joint Conference on Artifi cial Intelligence (2017). (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). (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). (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). (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). (578.43 KB)
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> (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). (2.64 MB)
Sridhar, D., Pujara, J. & Getoor, L. Using Noisy Extractions to Discover Causal Knowledge. NIPS Workshop on Automated Knowledge Base Construction (2017). (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). (711.26 KB)

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