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

broecheler-nips10.pdf (382.51 KB)

M. Broecheler, Mihalkova, L., and Getoor, L., “Probabilistic Similarity Logic”, in Conference on Uncertainty in Artificial Intelligence, 2010.

broecheler-uai10.pdf (399.54 KB)

M. Broecheler and Getoor, L., “Probabilistic Similarity Logic”, in International Workshop on Statistical Relational Learning (SRL'09), 2009.

broecheler-srl09.pdf (176.13 KB)

S. Bradley and Getoor, L., “Topic Modeling for Wikipedia Link Disambiguation”, ACM Transactions on Information Systems, vol. 32, 2014.

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.

bilgic11a.pdf (1.64 MB)

M. Bilgic and Getoor, L., “Active Inference for Collective Classification”, in Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track), 2010, pp. 1652–1655.

bilgic-aaai10.pdf (387.53 KB)

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.

bilgic-icml10.pdf (515.65 KB)

M. Bilgic, “Information Acquisition in Structured Domains”, University of Maryland - College Park, 2010.

mbilgic-phdthesis.pdf (4.68 MB)

M. Bilgic and Getoor, L., “Link-based Active Learning”, in NIPS Workshop on Analyzing Networks and Learning with Graphs, 2009.

mbilgic-nips09wkshp.pdf (116.35 KB)

M. Bilgic and Getoor, L., “Reflect and Correct: A Misclassification Prediction Approach to Active Inference”, ACM Transactions on Knowledge Discovery from Data, vol. 3, pp. 1–32, 2009.

bilgic-tkdd09.pdf (3.66 MB)

M. Bilgic and Getoor, L., “Effective Label Acquisition for Collective Classification”, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008, pp. 43–51.

bilgic-kdd08.pdf (758.14 KB)

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

mgcs07.pdf (105.13 KB)

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.

bilgic-aaai07.pdf (220.47 KB)

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

M. Bilgic, Licamele, L., Getoor, L., and Shneiderman, B., “D-Dupe: An Interactive Tool for Entity Resolution in Social Networks”, in International Symposium on Graph Drawing, 2005, vol. 3843, pp. 505–507.

ddupe.pdf (224.93 KB)

I. Bhattacharya and Getoor, L., Collective Relational Clustering, 1st ed., vol. 1. Chapman and Hall, 2008, pp. 221-244.

bhattacharya-book08.pdf (10.39 MB)

I. Bhattacharya and Getoor, L., “Collective Entity Resolution In Relational Data”, ACM Transactions on Knowledge Discovery from Data, vol. 1, pp. 1-36, 2007.

bhattacharya-tkdd.pdf (346.13 KB)

I. Bhattacharya and Getoor, L., “Online Collective Entity Resolution”, in The 22nd National Conference on Artificial Intelligence (NECTAR Track), 2007.

nectar07.pdf (395.24 KB)

I. Bhattacharya and Getoor, L., “Query-time Entity Resolution”, Journal of Artificial Intelligence Research (JAIR), vol. 30, pp. 621–657, 2007.

bhattacharya07a.pdf (309.63 KB)

I. Bhattacharya and Getoor, L., “A Latent Dirichlet Model for Unsupervised Entity Resolution”, in SIAM Conference on Data Mining (SDM), 2006.

bhattacharyasdm06.pdf (209.24 KB)

I. Bhattacharya, “Collective Entity Resolution In Relational Data”, University of Maryland, College Park, 2006.

thesis.pdf (761.21 KB)

I. Bhattacharya and Getoor, L., “Collective Entity Resolution in Relational Data”, Data Engineering Bulletin, vol. 29, 2006.

I. Bhattacharya and Getoor, L., “Entity Resolution in Social Networks”, in International Sunbelt Social Network Conference (Sunbelt XXVI), 2006.

I. Bhattacharya and Getoor, L., Entity Resolutions in Graphs, 1st ed., vol. 1. Wiley, 2006, p. 311--344.

bhattacharya-book06.pdf (482.5 KB)

I. Bhattacharya, Licamele, L., and Getoor, L., “Query-Time Entity Resolution”, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006.

kdd06.pdf (183.45 KB)

I. Bhattacharya, Licamele, L., and Getoor, L., “Relational Clustering for Entity Resolution Queries”, in ICML Workshop on Statistical Relational Learning (SRL), 2006.

bhattacharyaicml06-wkshp.pdf (195.79 KB)

I. Bhattacharya and Getoor, L., “Relational Clustering for Multi-type Entity Resolution”, in ACM SIGKDD Workshop on Multi Relational Data Mining (MRDM), 2005.

bhattacharyakdd05-whskp.pdf (259.82 KB)

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

bhattacharyakdd04-whskp.pdf (231.67 KB)

I. Bhattacharya and Getoor, L., “Iterative Record Linkage for Cleaning and Integration”, in ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), 2004.

bhattacharyasigmod04-wkshp.pdf (222.38 KB)

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.

acl04.pdf (156.26 KB)

H. Bert, Stephen, B., Eric, N., Jay, P., and Getoor, L., “Social Group Modeling with Probabilistic Soft Logic”, in NeuRIPS Workshop on SNSMA, 2012.

huang-snsma12.pdf (1.02 MB)

V. Barash, Smith, M., Getoor, L., and Welser, H., “Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context”, in International Conference on Weblogs and Social Media, 2009.

barash-icwsm09.pdf (171.16 KB)

S. H. Bach, Huang, B., and Getoor, L., “Probabilistic Soft Logic for Social Good”, in KDD Workshop on Data Science for Social Good, 2014.

bach-dssg14.pdf (124.88 KB)

S. H. Bach, Huang, B., London, B., and Getoor, L., “Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction”, in Uncertainty in Artificial Intelligence, 2013.

bach-uai13.pdf (379.45 KB)

S. H. Bach, Huang, B., and Getoor, L., “Large-margin Structured Learning for Link Ranking”, in NIPS Workshop on Frontiers of Network Analysis: Methods, Models, and Applications, 2013.

bach-fna13.pdf (210.09 KB)

S. H. Bach, Huang, B., and Getoor, L., “Learning Latent Groups with Hinge-loss Markov Random Fields”, in ICML Workshop on Inferning: Interactions between Inference and Learning, 2013.

bach-inferning13.pdf (348.79 KB)

S. Bach, Broecheler, M., Getoor, L., and O'Leary, D., “Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization”, in NeuRIPS, 2012.

bach-nips12.pdf (274.58 KB)

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

bach-pmpm10.pdf (246.79 KB)

S. H. Bach, Broecheler, M., Huang, B., and Getoor, L., “Hinge-Loss Markov Random Fields and Probabilistic Soft Logic”, Journal of Machine Learning Research (JMLR), vol. 18, pp. 1-67, 2017.

bach-jmlr17.pdf (731.56 KB)

S. H. Bach, Broecheler, M., Huang, B., and Getoor, L., “Hinge-Loss Markov Random Fields and Probabilistic Soft Logic”, ArXiv:1505.04406 [cs.LG], 2015.

bach-arxiv15.pdf (686.27 KB)

S. H. Bach, “Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction”, University of Maryland, College Park, 2015.

bach-thesis15.pdf (1.17 MB)

S. H. Bach, Huang, B., Boyd-Graber, J., and Getoor, L., “Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs”, in International Conference on Machine Learning (ICML), 2015.

bach-icml15.pdf (356.46 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.

bach-aistats15.pdf (345.2 KB)

S. H. Bach, Huang, B., and Getoor, L., “Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies”, in NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML), 2014.

bach-discml14.pdf (254.9 KB)