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

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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)
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L. Singh, Beard, M., Getoor, L., and M. Blake, B., Visual mining of multi-modal social networks at different abstraction levelsx, in L. Singh, M. Beard, L. Getoor, M. Blake. Visual mining of multi-modal social networks at different abstraction levels. IEEE Conference on Information Visualization - Symposium of Visual Data Mining (IV-VDM), 2007.PDF icon singh2007IV.pdf (809.15 KB)
H. Kang, Getoor, L., and Singh, L., Visual Analysis of Dynamic Group Membership in Temporal Social Networks, SIGKDD Explorations, Special Issue on Visual Analytics, vol. 9, pp. 13-21, 2007.PDF icon 2_kang-CGROUP_1207.pdf (1.48 MB)
M. Bilgic and Getoor, L., Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition, Journal of Artificial Intelligence Research (JAIR), vol. 41, pp. 69–95, 2011.PDF icon bilgic11a.pdf (1.64 MB)
M. Bilgic and Getoor, L., VOILA: Efficient Feature-value Acquisition for Classification, in AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence, 2007.PDF icon bilgic-aaai07.pdf (220.47 KB)
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U. Chajewska, Getoor, L., and Norman, J., Utility Elicitation as a Classification Problem, in Proceedings of the AAAI Spring Symposium Series on Interactive and Mixed Initiative Decision-Theoretic Systems, 1998.
U. Chajewska, Getoor, L., Norman, J., and Shahar, Y., Utility Elicitation as a Classi cation Problem, in Uncertainty in Arti cial Intelligence, 1998.
K. Lerman, Getoor, L., Minton, S., and Knoblock, C., Using the Structure of Web Sites for Automatic Segmentation of Tables, in In Proceedings of ACM-SIGMOD 2004 International Conference on Management of Data, 2004.PDF icon lerman-sigmod04.pdf (307.43 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)
L. Getoor and Sahami, M., Using Probabilistic Relational Models for Collaborative Filtering, in Working Notes of the KDD Workshop on Web Usage Analysis and User Profiling, 1999.
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)
E. Zheleva, Getoor, L., Golbeck, J., and Kuter, U., Using Friendship Ties and Family Circles for Link Prediction, in 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD), 2008.PDF icon zheleva-snakdd08.pdf (656.31 KB)
M. desJardins, Getoor, L., and Koller, D., Using Feature Hierarchies in Bayesian Network Learning, in Symposium on Abstraction, Reformulation and Approximation, 2000.
J. Pujara, III, H. Daume, and Getoor, L., Using Classifier Cascades for Scalable E-Mail Classification, in Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, 2011.PDF icon pujara_ceas2011_camera.pdf (308.42 KB)
U. Chajewska, Norman, J., and Getoor, L., Using Classi cation Techniques for Utility Elicitation: A Comparison between StandardGamble and Visual Analog Scale Methods, in Twentieth Anniversary Meeting of the Society for Medical Decision Making, 1998.
R. Arti, Jaebong, Y., Shitian, S., Getoor, L., and Jihie, K., User Role Prediction in Online Discussion Forums using Probabilistic Soft Logic, in NeuRIPS Workshop on PE, 2012.PDF icon ramesh-pe11.pdf (40.65 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)
I. Bhattacharya, Getoor, L., and Bengio, Y., Unsupervised Sense Disambiguation using Bilingual Probabilistic Models, in Annual Meeting of the Association for Computational Linguistics (ACL), 2004.PDF icon acl04.pdf (156.26 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)
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)
L. Getoor, Rhee, J., Koller, D., and Small, P., Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models, AI in Medicine Journal, vol. 30, pp. 233-256, 2004.
A. Ramesh, Goldwasser, D., Huang, B., Daume, III, H., and Getoor, L., Understanding MOOC Discussion Forums using Seeded LDA, in ACL Workshop on Innovative Use of NLP for Building Educational Applications, 2014.PDF icon ramesh-aclws14.pdf (137.57 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. 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)
A. Ramesh and Getoor, L., Understanding Evolution of Long-running MOOCs, in International Conference on Web Information Systems Engineering (WISE), 2018.
H. Sharara, Singh, L., Getoor, L., and Mann, J., Understanding Actor Loyalty to Event-Based Groups in Affiliation Networks, Journal of Advances in Social Networks Analysis and Mining, vol. 1, pp. 115–126, 2011.
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs, in ACM Conference on Learning at Scale, 2014.
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E. Zheleva, Kolcz, A., and Getoor, L., Trusting Spam Reporters: A Reporter-based Reputation System for Email Filtering, ACM Transactions on Information Systems, vol. 27, 2008.PDF icon zheleva-tois08.pdf (447.31 KB)
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)
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.PDF icon embar-tpm19.pdf (410.24 KB)
S. Bradley and Getoor, L., Topic Modeling for Wikipedia Link Disambiguation, ACM Transactions on Information Systems, vol. 32, 2014.
E. Zheleva and Getoor, L., To Join or not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles, in 18th International World Wide Web conference (WWW), 2009.PDF icon fp660-zheleva.pdf (538.92 KB)
E. Zheleva and Getoor, L., To Join or not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles, in The Web Conference (WWW), College Park, 2009.PDF icon zheleva-cs-tr4926.pdf (366.68 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)
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)
S. Srinivasan, Augustine, E., and Getoor, L., Tandem Inference: An Out-of-Core Streaming Algorithm For Very Large-Scale Relational Inference, in AAAI Conference on Artificial Intelligence (AAAI), 2020.PDF icon srinivasan-aaai20b.pdf (506.62 KB)
P. Panagiotis, Panayiotis, T., Ariel, F., and Getoor, L., TACI: Taxonomy-Aware Catalog Integration, TKDE, vol. 25, 2012.PDF icon papadimitriou-tkde12.pdf (2.93 MB)
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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. Somasundaran, Namata, G. Mark, Wiebe, J., and Getoor, L., Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification, in Conference on Empirical Methods in Natural Language Processing, 2009.PDF icon somasundaran-emnlp09.pdf (390.93 KB)
W. Eldin Moustafa, Kimmig, A., Deshpande, A., and Getoor, L., Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty, in International Conference on Data Engineering (ICDE), 2014.PDF icon ICDE14_conf_full_374.pdf (1.57 MB)
T. Dietterich, Domingos, P., Getoor, L., Muggleton, S., and Tadepalli, P., Structured machine learning: the next ten years, Machine Learning, vol. 73, pp. 3–23, 2008.
L. Getoor, Structure Discovery Using Statistical Relational Learning, Data Engineering Bulletin, vol. 26, p. 11- -18, 2003.
B. London, Huang, B., and Getoor, L., On the Strong Convexity of Variational Inference, in NIPS Workshop on Advances in Variational Inference, 2014.PDF icon london-nips14ws.pdf (253.72 KB)
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)
S. Hossam, Lisa, S., Getoor, L., and Janet, M., Stability vs. Diversity: Understanding the Dynamics of Actors in Time-varying Affiliation Networks, in ICSI, 2012.PDF icon sharara-icsi12.pdf (307.98 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, 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)

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