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Plangprasopchok, A., Lerman, K. & Getoor, L. A Probabilistic Approach for Learning Folksonomies from Structured Data. Fourth ACM International Conference on Web Search and Data Mining (WSDM) (2011).

Plangprasopchok, A., Lerman, K. & Getoor, L. Growing a tree in the forest: constructing folksonomies by integrating structured metadata. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010).

plang-kdd10.pdf (705.71 KB)

Polymeropoulos, M. et al. Common effect of antipsychotics on the biosynthesis and regulation of fatty acids and cholesterol supports a key role of lipid homeostasis in schizophrenia. Schizophrenia Research (2009).

Pujara, J., Miao, H. & Getoor, L. Joint Judgments with a Budget: Strategies for Reducing the Cost of Inference. ICML Workshop on Machine Learning with Test-Time Budgets (2013).

pujara_wtbudg13.pdf (221.26 KB)

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Knowledge Graph Identification. International Semantic Web Conference (ISWC) (2013).

pujara_iswc13.pdf (508.7 KB)

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Large-Scale Knowledge Graph Identification using PSL. ICML Workshop on Structured Learning (SLG) (2013).

pujara_slg13.pdf (277.63 KB)

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Large-Scale Knowledge Graph Identification using PSL. AAAI Fall Symposium on Semantics for Big Data (2013).

pujara_s4bd13.pdf (306.96 KB)

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Ontology-Aware Partitioning for Knowledge Graph Identification. CIKM Workshop on Automatic Knowledge Base Construction (2013).

pujara_akbc13.pdf (370.62 KB)

Pujara, J. & Skomoroch, P. Large-Scale Hierarchical Topic Models. NIPS Workshop on Big Learning (2012).

pujara_biglearn12.pdf (189.96 KB)

Pujara, J., London, B. & Getoor, L. Reducing Label Cost by Combining Feature Labels and Crowdsourcing. ICML Workshop on Combining Learning Strategies to Reduce Label Cost (2011).

clsicml_pujara_london.pdf (253.29 KB)

Pujara, J., III, H. Daume & Getoor, L. Using Classifier Cascades for Scalable E-Mail Classification. Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (ACM, 2011).

pujara_ceas2011_camera.pdf (308.42 KB)

Pujara, J. & Getoor, L. Coarse-to-Fine, Cost-Sensitive Classification of E-Mail. NIPS Workshop on Coarse-to-Fine Processing (2010).

pujara_nips10.pdf (258.86 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>

pujara-emnlp17.pdf (677.74 KB)

Pujara, J., London, B. & Getoor, L. Budgeted Online Collective Inference. Uncertainty in Artificial Intelligence (2015).

pujara-uai15.pdf (302.63 KB)

Pujara, J., London, B., Getoor, L. & Cohen, W. Online Inference for Knowledge Graph Construction. Workshop on Statistical Relational AI (2015).

pujara-starai15.pdf (340.95 KB)

Pujara, J., Miao, H., Getoor, L. & Cohen, W. Using Semantics & Statistics to Turn Data into Knowledge. AI Magazine 36, 65–74 (2015).

pujara_aimag15.pdf (359.48 KB)

Pujara, J. Probabilistic Models for Scalable Knowledge Graph Construction. (2016).

pujara-thesis15.pdf (1.06 MB)

Pujara, J. & Getoor, L. Generic Statistical Relational Entity Resolution in Knowledge Graphs. Sixth International Workshop on Statistical Relational AI (IJCAI 2016, 2016).

pujara-starai16.pdf (151.37 KB)

Pujara, J. & Getoor, L. Building Dynamic Knowledge Graphs. NIPS Workshop on Automated Knowledge Base Construction (2014).

pujara_akbc14.pdf (143.26 KB)

R

Ramakrishnan, N. et al. ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2014).

ramakrishnan-kdd14.pdf (1.15 MB)

Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Learning Latent Engagement Patterns of Students in Online Courses. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI Press, 2014).

ramesh-aaai14.pdf (505.47 KB)

Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs. ACM Conference on Learning at Scale (ACM, 2014).

Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Understanding MOOC Discussion Forums using Seeded LDA. 9th ACL Workshop on Innovative Use of NLP for Building Educational Applications (ACL, 2014).

ramesh-aclws14.pdf (137.57 KB)

Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic. NIPS Workshop on Data Driven Education (2013).

ramesh-nipsws13.pdf (153.92 KB)

Ramesh, A., Yoo, J., Shen, S., Getoor, L. & Kim, J. User Role Prediction in Online Discussion Forums using Probabilistic Soft Logic. (2012).

Arti_nips_2012_final_version_1.pdf (759.57 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).

ramesh-icwi17.pdf (761.17 KB)

Ramesh, A., Rodriguez, M. & Getoor, L. Understanding Influence in Online Professional Networks. NIPS Workshop on Networks in Social and Information Sciences (2015).

ramesh-nipsws15.pdf (211.44 KB)

Ramesh, A., Kumar, S., Foulds, J. & Getoor, L. Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums. 53rd Annual Meeting of the Association for Computational Linguistics (ACL) (2015).

ramesh-acl15.pdf (168.7 KB)

Ramesh, A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. (2016).

ramesh-thesis16.pdf (865.41 KB)

Ramesh, A. & Getoor, L. Understanding Evolution of Long-running MOOCs. International Conference on Web Information Systems Engineering (WISE) (2018).

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).

ramesh-tlt19.pdf (4.3 MB)

Rastegari, M., Choi, J., Fakhraei, S., III, H. Daume & Davis, L. Predictable Dual-View Hashing. Proceedings of the 30th International Conference on Machine Learning (ICML-13) 1328–1336 (JMLR, 2013).

rastegari13.pdf (2.35 MB)

Rekatsinas, T., Deshpande, A. & Getoor, L. Local Structure and Determinism in Probabilistic Databases. ACM SIGMOD Conference (2012).

sigmod_AAC2012.pdf (490.28 KB)

Rekatsinas, T., Dong, X. Luna, Getoor, L. & Srivastava, D. Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. 7th Biennial Conference on Innovative Data Systems Research (CIDR `15) (2015).

rekatsinasCIDR15.pdf (396.99 KB)

Rekatsinas, T. Quality-Aware Data Source Management. (2015).

Rekatsinas, T. et al. SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources. 2015 SIAM International Conference on Data Mining (SDM15) (SIAM, 2015).

rekatsinasSDM2015.pdf (303.08 KB)

Rekatsinas, T., Deshpande, A., Dong, X. Luna, Getoor, L. & Srivastava, D. SourceSight: Enabling Effective Source Selection. ACM SIGMOD Conference (2016).

modde087.pdf (799.94 KB)

Rekatsinas, T. et al. Forecasting Rare Disease Outbreaks from Open Source Indicators. Statistical Analysis and Data Mining: The ASA Data Science Journal (2016).

rekatsinas-sadm17.pdf (303.08 KB)

S

Saha, B. & Getoor, L. On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch. 2009 SIAM International Conference on Data Mining (SDM09) (2009).

saha-sdm08.pdf (233.12 KB)

Saha, B. & Getoor, L. Group Proximity Measure for Recommending Groups in Online Social Networks. 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD) (2008).

kddw-saha.pdf (311.36 KB)

Sayyadi, H. & Getoor, L. Future Rank: Ranking Scientific Articles by Predicting their Future PageRank. 2009 SIAM International Conference on Data Mining (SDM09) (2009).

sayyadi_futureRank_sdm09.pdf (621.55 KB)

Schnaitter, K., Polyzotis, N. & Getoor, L. Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications. International Conference on Very Large Data Bases (2009).

schnaitter-vldb09.pdf (743.29 KB)

Sehgal, V., Getoor, L. & Viechnicki, P. Entity Resolution in Geospatial Data Integration. ACM GIS (2006).

Sen, P., Namata, G. Mark, Bilgic, M. & Getoor, L. Collective Classification. Encyclopedia of Machine Learning (2010).

Sen, P., Deshpande, A. & Getoor, L. Read-Once Functions and Query Evaluation in Probabilistic Databases. International Conference on Very Large Data Bases (2010).

draft.pdf (322 KB)

Sen, P., Deshpande, A. & Getoor, L. Bisimulation-based Approximate Lifted Inference. Uncertainty in Artificial Intelligence (2009).

uai09.pdf (240.89 KB)

Sen, P., Deshpande, A. & Getoor, L. PrDB: Managing and Exploiting Rich Correlations in Probabilistic Databases. VLDB Journal, special issue on uncertain and probabilistic databases (2009).

sen-vldbj09.pdf (1.12 MB)

Sen, P. Representing and Querying Uncertain Data. (2009).

thesis.pdf (1.12 MB)

Sen, P. et al. Collective Classification in Network Data. AI Magazine 29, 93–106 (2008).

sen-aimag08.pdf (497.82 KB)

Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. Data Mining and Knowledge Discovery, Special Issue on Utility Based Data Mining 17, 136–163 (2008).

draft.pdf (424.09 KB)

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