Publications

Export 319 results:
Author [ Title(Desc)] 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 
A
A. Kimmig, Memory, A., Miller, R. J., and Getoor, L., A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence, IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 31, no. 8, p. 1426--1439, 2019.PDF icon kimming-tkde19.pdf (713.64 KB)
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, Babaki, B., and Getoor, L., A Declarative Approach to Fairness in Relational Domains, IEEE Data Engineering Bulletin, vol. 42, no. 3, p. 36--48, 2019.PDF icon farnadi-de19.pdf (365.14 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)
R. Islamaj, Getoor, L., and Wilbur, J., A Feature Generation Algorithm with Applications to Biological Sequence Classification, 1st ed., vol. 1. Chapman and Hall/CRC Press, 2008, p. 355--376.PDF icon islamaj-book08.pdf (3.09 MB)
B. Huang, Kimmig, A., Getoor, L., and Golbeck, J., A Flexible Framework for Probabilistic Models of Social Trust, in SBP, 2013.PDF icon huang-sbp13.pdf (247.2 KB)
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)
A. Kimmig, Bach, S., Broecheler, M., Huang, B., and Getoor, L., A Short Introduction to Probabilistic Soft Logic, in NIPS Workshop on PPFA, 2012.PDF icon kimming-ppfa12.pdf (164.6 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)
G. Namata, Sharara, H., and Getoor, L., A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks, 1st ed., vol. 1. Springer, 2010, p. 107--133.PDF icon namata-book10.pdf (656.83 KB)
A. Grycner, Weikum, G., Pujara, J., Foulds, J., and Getoor, L., A Unified Probabilistic Approach for Semantic Clustering of Relational Phrases, in NeurIPS, 2014.
M. Bilgic and Getoor, L., Active Inference for Collective Classification, in Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track), 2010, pp. 1652–1655.PDF icon bilgic-aaai10.pdf (387.53 KB)
C. Daozheng, Mustafa, B., Getoor, L., David, J., Lilyana, M., and Tom, Y., Active Inference for Retrieval in Camera Networks, in IEEE Workshop on Person-Oriented Vision, 2011.PDF icon chen-wpov11.pdf (1.53 MB)
H. Sharara, Getoor, L., and Norton, M., An Active Learning Approach for Identifying Key Opinion Leaders, in The 2nd Workshop on Information in Networks (WIN), 2010.
M. Bilgic, Mihalkova, L., and Getoor, L., Active Learning for Networked Data, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010.PDF icon bilgic-icml10.pdf (515.65 KB)
H. Sharara, Getoor, L., and Norton, M., Active Surveying, in NIPS Workshop on Networks Across Disciplines in Theory and Applications, 2010.
S. Hossam, Getoor, L., and Myra, N., Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders, in IJCAI, 2011.PDF icon sharara-ijcai11.pdf (349.39 KB)
H. Sharara, Norton, M., and Getoor, L., Active Surveying for Leadership Identification, in The International Sunbelt Social Networks Conference XXX, 2010.
S. Fakhraei, Dhanya, S., Pujara, J., and Getoor, L., Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks, in KDD, 2016.
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)
B
S. Srinivasan, Farnadi, G., and Getoor, L., BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic, in AAAI Conference on Artificial Intelligence (AAAI), 2020.PDF icon srinivasan-aaai20a.pdf (478.65 KB)
M. desJardins, Rathod, P., and Getoor, L., Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence, in 16th European Conference on Machine Learning (ECML), 2005.
N. Ramakrishnan, Butler, P., Self, N., Khandpur, R., Saraf, P., Wang, W., Cadena, J., Vullikanti, A., Korkmaz, G., Kuhlman, C., Marathe, A., Zhao, L., Ting, H., Huang, B., Srinivasan, A., Trinh, K., Getoor, L., Katz, G., Doyle, A., Ackermann, C., Zavorin, I., Ford, J., Summers, K., Fayed, Y., Arredondo, J., Gupta, D., and Mares, D., ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators, in ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2014.PDF icon ramakrishnan-kdd14.pdf (1.15 MB)
P. Sen, Deshpande, A., and Getoor, L., Bisimulation-based Approximate Lifted Inference, in Uncertainty in Artificial Intelligence, 2009.PDF icon uai09.pdf (240.89 KB)
J. Pujara, London, B., and Getoor, L., Budgeted Online Collective Inference, in UAI, 2015.PDF icon pujara-uai15.pdf (302.63 KB)
J. Pujara and Getoor, L., Building Dynamic Knowledge Graphs, in NIPS Workshop on Automated Knowledge Base Construction, 2014.PDF icon pujara_akbc14.pdf (143.26 KB)
C
H. Kang, Getoor, L., and Singh, L., C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership, in Visual Analytics Science and Technology (VAST), 2007.PDF icon vast07-kang.pdf (663.26 KB)
L. Licamele, Bilgic, M., Getoor, L., and Roussopoulos, N., Capital and Benefit in Social Networks, in ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD), 2005.PDF icon licamele_linkkdd05.pdf (421.14 KB)
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)
B. Salami, Parikh, H., Kayali, M., Roy, S., Getoor, L., and Suciu, D., Causal Relational Learning, in International Conference on Management of Data (SIGMOD), 2020.PDF icon salami-sigmod20.pdf (1.02 MB)
J. Doppa, Yu, J., Tadepalli, P., and Getoor, L., Chance-Constrained Programs for Link Prediction, in NIPS Workshop on Analyzing Networks and Learning with Graphs, 2009.PDF icon doppa-nips09wkshp.pdf (161.38 KB)
R. Islamaj, Getoor, L., and W. Wilbur, J., Characterizing RNA secondary-structure features and their effects on splice-site prediction, in IEEE ICDM Workshop on Mining and Management of Biological Data, 2007.
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)
J. Pujara and Getoor, L., Coarse-to-Fine, Cost-Sensitive Classification of E-Mail, in NIPS Workshop on Coarse-to-Fine Processing, 2010.PDF icon pujara_nips10.pdf (258.86 KB)
E. Zheleva, Sharara, H., and Getoor, L., Co-evolution of Social and Affiliation Networks, in 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2009.PDF icon fp659-zheleva.pdf (900 KB)
A. Lansky, Friedman, M., Getoor, L., Schmidler, S., and Jr., N. Short, The Collage/Khoros Link: Planning for Image Processing Tasks, in Proceedings of the AAAI Spring Symposium on Integrated Planning Applications, 1995.
B. London, Khamis, S., Bach, S., Huang, B., Getoor, L., and Davis, L., Collective Activity Detection using Hinge-loss Markov Random Fields, in CVPR Workshop on SPTLE, 2013.PDF icon london-sptle13.pdf (705.87 KB)
V. Embar, Pujara, J., and Getoor, L., Collective Alignment of Large-scale Ontologies, in AKBC Workshop on Federated Knowledge Bases (FKBs), 2019.PDF icon embar-fkbs19.pdf (57.36 KB)
P. Sen, Namata, G. Mark, Bilgic, M., and Getoor, L., Collective Classification, Encyclopedia of Machine Learning, 2010.
G. Namata, Sen, P., Bilgic, M., and Getoor, L., Collective Classification for Text Classification, 1st ed., vol. 1. Taylor and Francis Group, 2009, p. 51--69.PDF icon namata-book09.pdf (4.35 MB)
P. Sen, Namata, G. Mark, Bilgic, M., Getoor, L., Gallagher, B., and Eliassi-Rad, T., Collective Classification in Network Data, AI Magazine, vol. 29, pp. 93–106, 2008.PDF icon sen-aimag08.pdf (497.82 KB)
B. London and Getoor, L., Collective Classification of Network Data, 1st ed., vol. 1. CRC Press, 2013, p. 399--416.PDF icon london-book13.pdf (394.37 KB)
I. Bhattacharya and Getoor, L., Collective Entity Resolution In Relational Data, ACM Transactions on Knowledge Discovery from Data, vol. 1, pp. 1-36, 2007.PDF icon bhattacharya-tkdd.pdf (346.13 KB)
I. Bhattacharya, Collective Entity Resolution In Relational Data, University of Maryland, College Park, 2006.PDF icon thesis.pdf (761.21 KB)
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)
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)
I. Bhattacharya and Getoor, L., Collective Entity Resolution in Relational Data, Data Engineering Bulletin, vol. 29, 2006.
G. Namata, Kok, S., and Getoor, L., Collective Graph Identification, in KDD, 2011.PDF icon namata-kdd11.pdf (185.7 KB)

Pages