Publications

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Bach, S. H., Huang, B. & Getoor, L. Probabilistic Soft Logic for Social Good. KDD Workshop on Data Science for Social Good (2014). (124.88 KB)
Bach, S. H., Huang, B., London, B. & Getoor, L. Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. Uncertainty in Artificial Intelligence (2013). (379.45 KB)
Bach, S. H., Huang, B. & Getoor, L. Large-margin Structured Learning for Link Ranking. NIPS Workshop on Frontiers of Network Analysis: Methods, Models, and Applications (2013). (210.09 KB)
Bach, S. H., Huang, B. & Getoor, L. Learning Latent Groups with Hinge-loss Markov Random Fields. ICML Workshop on Inferning: Interactions between Inference and Learning (2013). (348.79 KB)
Bach, S. H., Broecheler, M., Getoor, L. & O'Leary, D. P. Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization. Advances in Neural Information Processing Systems (NIPS) 2663–2671 (2012). (274.58 KB)
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). (246.79 KB)
Bach, S. H., Broecheler, M., Huang, B. & Getoor, L. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. Journal of Machine Learning Research (JMLR) 18, 1-67 (2017). (731.56 KB)
Bach, S. H., Broecheler, M., Huang, B. & Getoor, L. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. ArXiv:1505.04406 [cs.LG] (2015). (686.27 KB)
Bach, S. H. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction. (2015). (1.17 MB)
Bach, S. H., Huang, B., Boyd-Graber, J. & Getoor, L. Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. International Conference on Machine Learning (ICML) (2015). (356.46 KB)
Bach, S. H., Huang, B. & Getoor, L. Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. Artificial Intelligence and Statistics (AISTATS) (2015). (345.2 KB)
Bach, S. H., Huang, B. & Getoor, L. Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML) (2014). (254.9 KB)
Barash, V., Smith, M., Getoor, L. & Welser, H. Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context. International Conference on Weblogs and Social Media (2009). (171.16 KB)
Bhattacharya, I. & Getoor, L. CRC Data Mining Series 223-243 (Chapman and Hall, 2008).
Bhattacharya, I. & Getoor, L. Collective Entity Resolution In Relational Data. ACM Transactions on Knowledge Discovery from Data 1, 1-36 (2007). (346.13 KB)
Bhattacharya, I. & Getoor, L. Online Collective Entity Resolution. The 22nd National Conference on Artificial Intelligence (NECTAR Track) (AAAI Press, 2007). (395.24 KB)
Bhattacharya, I. & Getoor, L. Query-time Entity Resolution. Journal of Artificial Intelligence Research (JAIR) 30, 621–657 (2007). (309.63 KB)
Bhattacharya, I. & Getoor, L. A Latent Dirichlet Model for Unsupervised Entity Resolution. SIAM Conference on Data Mining (SDM) (2006). (209.24 KB)
Bhattacharya, I. Collective Entity Resolution In Relational Data. (2006). (761.21 KB)
Bhattacharya, I. & Getoor, L. Collective Entity Resolution in Relational Data. Data Engineering Bulletin 29, (2006).
Bhattacharya, I. & Getoor, L. Entity Resolution in Social Networks. International Sunbelt Social Network Conference (Sunbelt XXVI) (2006).
Bhattacharya, I. & Getoor, L. Mining Graph Data (Cook, D. & Holder, L.) (Wiley, 2006).
Bhattacharya, I., Licamele, L. & Getoor, L. Query-Time Entity Resolution. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006). (183.45 KB)
Bhattacharya, I., Licamele, L. & Getoor, L. Relational Clustering for Entity Resolution Queries. ICML Workshop on Statistical Relational Learning (SRL) (2006). (195.79 KB)
Bhattacharya, I. & Getoor, L. Relational Clustering for Multi-type Entity Resolution. ACM SIGKDD Workshop on Multi Relational Data Mining (MRDM) (2005). (259.82 KB)
Bhattacharya, I. & Getoor, L. Deduplication and Group Detection using Links. ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD) (2004). (231.67 KB)
Bhattacharya, I. & Getoor, L. Iterative Record Linkage for Cleaning and Integration. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD) (2004). (222.38 KB)
Bhattacharya, I., Getoor, L. & Bengio, Y. Unsupervised Sense Disambiguation using Bilingual Probabilistic Models. Annual Meeting of the Association for Computational Linguistics (ACL) (2004). (156.26 KB)
Bilgic, M. & Getoor, L. Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition. Journal of Artificial Intelligence Research (JAIR) 41, 69–95 (2011). (1.64 MB)
Bilgic, M. & Getoor, L. Active Inference for Collective Classification. Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track) 1652–1655 (2010). (387.53 KB)
Bilgic, M., Mihalkova, L. & Getoor, L. Active Learning for Networked Data. Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010). (515.65 KB)
Bilgic, M. Information Acquisition in Structured Domains. (2010). (4.68 MB)
Bilgic, M. & Getoor, L. Link-based Active Learning. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009). (116.35 KB)
Bilgic, M. & Getoor, L. Reflect and Correct: A Misclassification Prediction Approach to Active Inference. ACM Transactions on Knowledge Discovery from Data 3, 1–32 (2009). (3.66 MB)
Bilgic, M. & Getoor, L. Effective Label Acquisition for Collective Classification. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 43–51 (2008). (758.14 KB)
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). (105.13 KB)
Bilgic, M. & Getoor, L. VOILA: Efficient Feature-value Acquisition for Classification. AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence (2007). (220.47 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). (224.93 KB)
Bradley, S. & Getoor, L. Topic Modeling for Wikipedia Link Disambiguation. ACM Transactions on Information Systems 32, (2014).
Broecheler, M. & Getoor, L. Computing marginal distributions over continuous Markov networks for statistical relational learning. Advances in Neural Information Processing Systems (NIPS) (2010). (382.51 KB)
Broecheler, M., Mihalkova, L. & Getoor, L. Probabilistic Similarity Logic. Conference on Uncertainty in Artificial Intelligence (2010). (399.54 KB)
Broecheler, M. & Getoor, L. Probabilistic Similarity Logic. International Workshop on Statistical Relational Learning (SRL'09) (2009). (176.13 KB)
C
Chajewska, U., Norman, J. & Getoor, L. Using Classi cation Techniques for Utility Elicitation: A Comparison between StandardGamble and Visual Analog Scale Methods. Twentieth Anniversary Meeting of the Society for Medical Decision Making (1998).
Chajewska, U., Getoor, L., Norman, J. & Shahar, Y. Utility Elicitation as a Classi cation Problem. Uncertainty in Arti cial Intelligence (1998).
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). (299.32 KB)
Chen, D. et al. Active Inference for Retrieval in Camera Networks. Workshop on Person Oriented Vision (2011). (1.53 MB)

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