Archived Publications (Latest: https://linqs.github.io/linqs-website/publications/)

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Author Title [ Year(Desc)]
2015
T. Rekatsinas, “Quality-Aware Data Source Management”, University of Maryland, College Park, 2015.
A. Grycner, Weikum, G., Pujara, J., Foulds, J., and Getoor, L., “RELLY: Inferring Hypernym Relationships Between Relational Phrases”, in Conference on Empirical Methods in Natural Language Processing, 2015.PDF icon agrycner-emnlp15.pdf (234.86 KB)
T. Rekatsinas, Ghosh, S., Mekaru, S., Nsoesie, E., Brownstein, J., Getoor, L., and Ramakrishnan, N., “SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources”, in 2015 SIAM International Conference on Data Mining (SDM15), 2015.PDF icon rekatsinasSDM2015.pdf (303.08 KB)
B. London, Huang, B., and Getoor, L., “Stability and Generalization in Structured Prediction”, –, 2015.PDF icon london-stability15.pdf (532.16 KB)
B. London, “On the Stability of Structured Prediction”, University of Maryland, 2015.PDF icon blondon-thesis.pdf (1.16 MB)
G. Farnadi, Bach, S. H., Blondeel, M., Moens, M. - F., Getoor, L., and De Cock, M., “Statistical Relational Learning with Soft Quantifiers”, in International Conference on Inductive Logic Programming (ILP), 2015.PDF icon farnadi-ilp15.pdf (578.43 KB)
B. London, Huang, B., and Getoor, L., “The Benefits of Learning with Strongly Convex Approximate Inference”, in ICML, 2015.PDF icon london-icml15.pdf (788.06 KB)
A. Ramesh, Rodriguez, M., and Getoor, L., “Understanding Influence in Online Professional Networks”, in NIPS Workshop on Networks in Social and Information Sciences, 2015.PDF icon ramesh-nipsws15.pdf (211.44 KB)
S. H. Bach, Huang, B., and Getoor, L., “Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees”, in Artificial Intelligence and Statistics (AISTATS), 2015.PDF icon bach-aistats15.pdf (345.2 KB)
J. Pujara, Miao, H., Getoor, L., and Cohen, W., “Using Semantics & Statistics to Turn Data into Knowledge”, AI Magazine, vol. 36, pp. 65–74, 2015.PDF icon pujara_aimag15.pdf (359.48 KB)
A. Ramesh, Kumar, S., Foulds, J., and Getoor, L., “Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums”, in 53rd Annual Meeting of the Association for Computational Linguistics (ACL), 2015.PDF icon ramesh-acl15.pdf (168.7 KB)
2016
D. Sridhar, Fakhraei, S., and Getoor, L., “A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction”, Bioinformatics, vol. 32, 2016.PDF icon sridhar-bioinformatics16.pdf (1.94 MB)
A. Ramesh, “A Probabilistic Approach to Modeling Socio-Behavioral Interactions”, University of Maryland, College Park, 2016.PDF icon ramesh-thesis16.pdf (865.41 KB)
S. Fakhraei, Dhanya, S., Pujara, J., and Getoor, L., “Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks”, in KDD, 2016.
S. Muthiah, Huang, B., Arredondo, J., Mares, D., Getoor, L., Katz, G., and Ramakrishnan, N., “Capturing Planned Protests from Open Source Indicators”, AI Mag, vol. 37, pp. 63–75, 2016.PDF icon muthiah-aimag16.pdf (1.23 MB)
P. Kouki, Marcum, C., Koehly, L., and Getoor, L., “Entity Resolution in Familial Networks”, in MLG, 2016.PDF icon kouki-mlg16.pdf (633.08 KB)
J. Pujara and Getoor, L., “Generic Statistical Relational Entity Resolution in Knowledge Graphs”, in StarAI, 2016.PDF icon pujara-starai16.pdf (151.37 KB)
D. Sridhar and Getoor, L., “Joint Probabilistic Inference of Causal Structure”, in KDD Workshop on CD, 2016.PDF icon sridhar-cd16.pdf (204.51 KB)
S. Tomkins, Ramesh, A., and Getoor, L., “Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study”, in EDM, 2016.PDF icon tomkins-edm16.pdf (619.77 KB)
D. Sridhar and Getoor, L., “Probabilistic Inference for Causal Structure Discovery”, in UAI Workshop on Causation, 2016.PDF icon sridhar-causation16.pdf (118.31 KB)
J. Pujara, “Probabilistic Models for Scalable Knowledge Graph Construction”, University of Maryland, College Park, 2016.PDF icon pujara-thesis15.pdf (1.06 MB)
T. Rekatsinas, Deshpande, A., Dong, L., Getoor, L., and Srivastava, D., “SourceSight: Enabling Effective Source Selection”, in SIGMOD, 2016.PDF icon rekatsinas-sigmod16.pdf (799.94 KB)
B. London, Huang, B., and Getoor, L., “Stability and Generalization in Structured Prediction”, Journal of Machine Learning Research, vol. 17, 2016.PDF icon london-jmlr17.pdf (532.8 KB)
S. Kumar, Pujara, J., Getoor, L., Mares, D., Gupta, D., and Riloff, E., “Unsupervised Models for Predicting Strategic Relations between Organizations”, in ASONAM, 2016.PDF icon kumar-asonam16.pdf (212.61 KB)
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)
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)

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