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D

Sharara, H., Rand, W. & Getoor, L. Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing. The Fifth International AAAI Conference on Weblogs and Social Media (2011).

adaptive_cascade_model.pdf (646.6 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).

tomkins-NIPSLLD17.pdf (286.95 KB)

Bhattacharya, I. & Getoor, L. Deduplication and Group Detection using Links. ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD) (2004).

bhattacharyakdd04-whskp.pdf (231.67 KB)

Moustafa, W. Eldin, Namata, G. Mark, Deshpande, A. & Getoor, L. Declarative Analysis of Noisy Information Networks. ICDE Workshop on Graph Data Management: Techniques and Applications (2011).

moustafa-gdm11.pdf (1.55 MB)

Bach, S. H., Broecheler, M., Kok, S. & Getoor, L. Decision-Driven Models with Probabilistic Soft Logic. NIPS Workshop on Predictive Models in Personalized Medicine (2010).

bach-pmpm10.pdf (246.79 KB)

Fakhraei, S., Onukwugha, E. & Getoor, L. Healthcare Data Analytics (Reddy, C. C. Aggarwa) (CRC Press, 2014).

Fakhraei, S., Onukwugha, E. & Getoor, L. Healthcare Data Analytics (Reddy, C. C. Aggarwa) (CRC Press, 2015).

fakhraei_book_2015.pdf (234.2 KB)

Bilgic, M., Licamele, L., Getoor, L. & Shneiderman, B. D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. Visual Analytics Science and Technology (VAST) (2006).

Bilgic, M., Licamele, L., Getoor, L. & Shneiderman, B. D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. International Symposium on Graph Drawing (Healy, P. & Nikolov, N. S.) 3843, 505–507 (Springer, 2005).

ddupe.pdf (224.93 KB)

C

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)

Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. SIAM Data Mining Workshop on Link Analysis, Counterterrorism and Security (2006).

sensiam_lacs06.pdf (137.37 KB)

Sen, P. & Getoor, L. Cost-Sensitive Learning with Conditional Markov Networks. International Conference on Machine Learning (2006).

senicml06.pdf (118.33 KB)

Broecheler, M. & Getoor, L. Computing marginal distributions over continuous Markov networks for statistical relational learning. Advances in Neural Information Processing Systems (NIPS) (2010).

broecheler-nips10.pdf (382.51 KB)

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>

augustine-sysml18.pdf (624.33 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).

Udrea, O. & Getoor, L. Combining statistical and logical inference for ontology alignment. Workshop on Semantic Web for Collaborative Knowledge Acquisition at the International Joint Conference on Artificial Intelligence (2007).

Bilgic, M., Namata, G. Mark & Getoor, L. Combining Collective Classification and Link Prediction. Workshop on Mining Graphs and Complex Structures at the IEEE International Conference on Data Mining (ICDM-2007) (2007).

mgcs07.pdf (105.13 KB)

Sridhar, D., Foulds, J., Huang, B., Walker, M. & Getoor, L. Collective classification of stance and disagreement in online debate forums. Bay Area Machine Learning Symposium (BayLearn) (2014).

Sridhar, D., Getoor, L. & Walker, M. Collective Stance Classification of Posts in Online Debate Forums. ACL Joint Workshop on Social Dynamics and Personal Attributes in Social Media (2014).

sridhar-aclws14.pdf (190.8 KB)

London, B., Huang, B., Taskar, B. & Getoor, L. Collective Stability in Structured Prediction: Generalization from One Example. Proceedings of the 30th International Conference on Machine Learning (ICML-13) (2013).

london-icml13-long.pdf (373.82 KB)

Fakhraei, S., Foulds, J., Shashanka, M. & Getoor, L. Collective Spammer Detection in Evolving Multi-Relational Social Networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (ACM, 2015).

fakhraei-kdd2015.pdf (573.89 KB)

Bhattacharya, I. & Getoor, L. CRC Data Mining Series 223-243 (Chapman and Hall, 2008).

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>

kimmig-icde17.pdf (463.69 KB)

Fakhraei, S., Huang, B. & Getoor, L. Collective Inference and Multi-Relational Learning for Drug–Target Interaction Prediction. NIPS Workshop on Machine Learning in Computational Biology (MLCB) (2013).

Namata, G. Mark, Kok, S. & Getoor, L. Collective Graph Identification. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011).

namata-kdd11.pdf (185.7 KB)

Namata, G. Mark, London, B. & Getoor, L. Collective Graph Identification. ACM Transactions on Knowledge Discovery from Data 10, 25:1–25:36 (2015).

namata-tkdd.pdf (500.96 KB)

Bhattacharya, I. & Getoor, L. Collective Entity Resolution in Relational Data. Data Engineering Bulletin 29, (2006).

Kouki, P., Pujra, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Multi-Relational Familial Networks. Knowledge and Information Systems (KAIS) (2018).

kouki-kais18.pdf (1.17 MB)

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>

kouki-icdm17.pdf (653.4 KB)

Bhattacharya, I. & Getoor, L. Collective Entity Resolution In Relational Data. ACM Transactions on Knowledge Discovery from Data 1, 1-36 (2007).

bhattacharya-tkdd.pdf (346.13 KB)

Bhattacharya, I. Collective Entity Resolution In Relational Data. (2006).

thesis.pdf (761.21 KB)

London, B. & Getoor, L. Data Classification: Algorithms and Applications (Aggarwal, C.) (CRC Press, 2013).

cc-chapter.pdf (394.37 KB)

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

sen-aimag08.pdf (497.82 KB)

Namata, G. Mark, Sen, P., Bilgic, M. & Getoor, L. Text Mining: Classification, Clustering, and Applications (Sahami, M. & Srivastava, A.) (Taylor and Francis Group, 2009).

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

Embar, V., Pujara, J. & Getoor, L. Collective Alignment of Large-scale Ontologies. AKBC Workshop on Federated KBs and the Open Knowledge Network (2019).

embar-akbc19.pdf (47.25 KB)

London, B. et al. Collective Activity Detection using Hinge-loss Markov Random Fields. CVPR Workshop on Structured Prediction: Tractability, Learning and Inference (2013).

london-cvpr13.pdf (705.87 KB)

Lansky, A., Friedman, M., Getoor, L., Schmidler, S. & Jr., N. Short. The Collage/Khoros Link: Planning for Image Processing Tasks. Proceedings of the AAAI Spring Symposium on Integrated Planning Applications (1995).

Zheleva, E., Sharara, H. & Getoor, L. Co-evolution of Social and Affiliation Networks. 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2009).

fp659-zheleva.pdf (900 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)

Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018).

chang-sysml18.pdf (299.32 KB)

Islamaj, R., Getoor, L. & W. Wilbur, J. Characterizing RNA secondary-structure features and their effects on splice-site prediction. IEEE ICDM Workshop on Mining and Management of Biological Data (2007).

Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Chance-Constrained Programs for Link Prediction. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).

doppa-nips09wkshp.pdf (161.38 KB)

Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Magazine 37, 63–75 (2016).

muthiah-aimag16.pdf (1.23 MB)

Licamele, L., Bilgic, M., Getoor, L. & Roussopoulos, N. Capital and Benefit in Social Networks. ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD) (2005).

licamele_linkkdd05.pdf (421.14 KB)

Kang, H., Getoor, L. & Singh, L. C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership. Visual Analytics Science and Technology (VAST) (2007).

vast07-kang.pdf (663.26 KB)

B

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

pujara_akbc14.pdf (143.26 KB)

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

pujara-uai15.pdf (302.63 KB)

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

uai09.pdf (240.89 KB)

London, B., Huang, B. & Getoor, L. The Benefits of Learning with Strongly Convex Approximate Inference. International Conference on Machine Learning (ICML) (2015).

london-icml15.pdf (788.06 KB)

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