Publications

Export 317 results:
[ Author(Desc)] Title Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
C
Chajewska, U., Getoor, L. & Norman, J. Utility Elicitation as a Classification Problem. Proceedings of the AAAI Spring Symposium Series on Interactive and Mixed Initiative Decision-Theoretic Systems (1998).
Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018).PDF icon chang-sysml18.pdf (299.32 KB)
Chen, D., Bilgic, M., Getoor, L. & Jacobs, D. Efficient Resource-constrained Retrospective Analysis of Long Video Sequences. NIPS Workshop on Adaptive Sensing, Active Learning and Experimental Design: Theory, Methods and Applications (2009).PDF icon chen-nips09-wkshp.pdf (383.38 KB)
D
Daozheng, C. et al. Active Inference for Retrieval in Camera Networks. IEEE Workshop on Person-Oriented Vision (2011).PDF icon chen-wpov11.pdf (1.53 MB)
Daozheng, C., Mustafa, B., Getoor, L. & David, J. Dynamic Processing Allocation in Video. PAMI 33, 2174-2187 (2011).PDF icon chen-pami11.pdf (1.16 MB)
Deshpande, A., Getoor, L. & Sen, P. Graphical Models for Uncertain Data. Managing and Mining Uncertain Data 1, 1--34 (Springer, 2009).PDF icon deshpande-book09.pdf (570.7 KB)
Diehl, C., Namata, G. Mark & Getoor, L. Relationship Identification for Social Network Discovery. AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence (2007).PDF icon diehl-aaai07.pdf (139.6 KB)
Diehl, C., Getoor, L. & Namata, G. Mark. Name Reference Resolution in Organizational Email Archives. SIAM Conference on Data Mining (SDM) (2006).PDF icon diehlsdm06.pdf (971.2 KB)
Dietterich, T., Domingos, P., Getoor, L., Muggleton, S. & Tadepalli, P. Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008).
Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Learning Algorithms for Link Prediction based on Chance Constraints. European Conference on Machine Learning (ECML) (2010).PDF icon doppa-ecml10.pdf (203 KB)
Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Chance-Constrained Programs for Link Prediction. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).PDF icon doppa-nips09wkshp.pdf (161.38 KB)
F
Fakhraei, S., Huang, B., Raschid, L. & Getoor, L. Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2014).PDF icon fakhraei-tcbb2014_accepted.pdf (3.97 MB)
Fakhraei, S., Huang, B. & Getoor, L. Collective Inference and Multi-Relational Learning for Drug–Target Interaction Prediction. NIPS Workshop on MLCB (2013).PDF icon fakhraei-mlcb13.pdf (243.86 KB)
Fakhraei, S., Raschid, L. & Getoor, L. Drug-Target Interaction Prediction for Drug Repurposing with Probabilistic Similarity Logic. KDD Workshop on BIOKDD (ACM, 2013).PDF icon fakhraei-biokdd13.pdf (669.27 KB)
Fakhraei, S., Foulds, J., Shashanka, M. & Getoor, L. Collective Spammer Detection in Evolving Multi-Relational Social Networks. KDD (2015).PDF icon fakhraei-kdd2015.pdf (573.89 KB)
Fakhraei, S., Onukwugha, E. & Getoor, L. Data Analytics for Pharmaceutical Discoveries. Healthcare Data Analytics 1, 1--25 (CRC Press, 2015).PDF icon fakhraei-book15.pdf (234.2 KB)
Fakhraei, S., Dhanya, S., Pujara, J. & Getoor, L. Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. KDD (ACM SIGKDD, 2016).
Farnadi, G., Bach, S. H., Moens, M. - F., Getoor, L. & De Cock, M. Extending PSL with Fuzzy Quantifiers. International Workshop on Statistical Relational Artificial Intelligence (StaRAI) (2014).PDF icon farnadi-starai14.pdf (196.15 KB)
Farnadi, G., Bach, S. H., Moens, M. - F., Getoor, L. & De Cock, M. Soft quantification in statistical relational learning. Machine Learning Journal (2017).PDF icon farnadi-mlj17.pdf (1.24 MB)
Farnadi, G. et al. Statistical Relational Learning with Soft Quantifiers. International Conference on Inductive Logic Programming (ILP) (2015).PDF icon farnadi-ilp15.pdf (578.43 KB)
Farnadi, G., Babaki, B. & Getoor, L. Fairness-aware Relational Learning and Inference. AAAI Workshop on DeLBP (2018).
Farnadi, G., Babaki, B. & Getoor, L. Fairness in Relational Domains. AAAI/ACM Conference on AIES (2018).PDF icon farnadi_aies2018.pdf (418.24 KB)
Farnadi, G., Kouki, P., Thompson, S. K., Srinivasan, S. & Getoor, L. A Fairness-aware Hybrid Recommender System. RecSys Workshop on FATREC (2018).PDF icon farnadi-fatrec18.pdf (474.04 KB)
Farnadi, G., Babaki, B. & Getoor, L. A Declarative Approach to Fairness in Relational Domains. TCDE-Bulletin 42, 36--48 (2019).PDF icon farnadi-de19.pdf (365.14 KB)
Foulds, J., Kumar, S. & Getoor, L. Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models. International Conference on Machine Learning (ICML) (2015).PDF icon Foulds2015LatentTopicNetworks.pdf (382.53 KB)
Friedman, N. & Getoor, L. Efficient Learning Using Constrained Sufficient Statistics. Uncertainty99 (1999).
Friedman, N., Getoor, L., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. International Joint Conference on Arti cial Intelligence (1999).PDF icon icjai99.pdf (156.94 KB)
G
Getoor, L. & Machanavajjhala, A. Entity Resolution in Big Data. KDD (2013).PDF icon getoor-kdd13.pdf (7.16 MB)
Getoor, L. & Machanavajjhala, A. Entity Resolution for Social Network Analysis and Mining. IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012).
Getoor, L. & Machanavajjhala, A. Entity Resolution: Theory, Practice & Open Challenges. International Conference on Very Large Data Bases (2012).PDF icon p2018_lisegetoor_vldb2012.pdf (89.45 KB)
Getoor, L. & Machanavajjhala, A. Entity Resolution: Theory, Practice, and Open Challenges. AAAI Conference on Artificial Intelligence (2012).
Getoor, L. & Mihalkova, L. Exploiting Statistical and Relational Information on the Web and in Social Media. (2011).PDF icon getoor-sdm11.pdf (88.88 KB)
Getoor, L. & Scheffer, T. Proceedings of the 28th International Conference on Machine Learning. Proceedings of the 28th International Conference on Machine Learning (2011).
Getoor, L. & Taskar, B. Introduction to Statistical Relational Learning. (The MIT Press, 2007).
Getoor, L., Friedman, N., Koller, D., Pfeffer, A. & Taskar, B. Probabilistic Relational Models. An Introduction to Statistical Relational Learning 1, 129--174 (MIT Press, 2007).PDF icon getoor-book07.pdf (648.15 KB)
Getoor, L. An Introduction to Probabilistic Graphical Models for Relational Data. Data Engineering Bulletin 29, (2006).
Getoor, L. & Grant, J. PRL: A Logical Approach to Probabilistic Relational Models. Machine Learning Journal 62, (2006).PDF icon getoor-mlj06.pdf (685.04 KB)
Getoor, L. & Diehl, C. Link Mining: A Survey. SigKDD Explorations Special Issue on Link Mining 7, (2005).
Getoor, L. Link-based Classification. Advanced Methods for Knowledge Discovery from Complex Data 1, 189--207 (Springer-Verlag, 2005).PDF icon getoor-book05.pdf (273.43 KB)
Getoor, L., Rhee, J., Koller, D. & Small, P. Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models. AI in Medicine Journal 30, 233-256 (2004).
Getoor, L. Link Mining: A New Data Mining Challenge. SIGKDD Explorations, volume 5, 85- -89 (2003).
Getoor, L. Structure Discovery Using Statistical Relational Learning. Data Engineering Bulletin 26, 11- -18 (2003).
Getoor, L., Friedman, N., Koller, D. & Taskar, B. Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research 3, 679- -707 (2002).PDF icon jmlr02.pdf (502.22 KB)

Pages