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A

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)

Augustine, E. & Farnadi, G. MLTrain: Collective Reasoning With Probabilistic Soft Logic. (2018). at <https://github.com/linqs/psl-examples/tree/uai18>

MLTrain - UAI 2018.pdf (8.93 MB)

Augustine, E., Rekatsinas, T. & Getoor, L. Tractable Probabilistic Reasoning Through Effective Grounding. Third ICML workshop on Tractable Probabilistic Modeling (2019).

augustine-tpm19.pdf (224.8 KB)

B

Bach, S. H., Huang, B. & Getoor, L. Probabilistic Soft Logic for Social Good. KDD Workshop on Data Science for Social Good (2014).

bach-dssg14.pdf (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).

bach-uai13.pdf (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).

bach-fna13.pdf (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).

bach-inferning13.pdf (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).

bach-nips12.pdf (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).

bach-pmpm10.pdf (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).

bach-jmlr17.pdf (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).

bach-arxiv15.pdf (686.27 KB)

Bach, S. H. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction. (2015).

bach-thesis15.pdf (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).

bach-icml15.pdf (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).

bach-aistats15.pdf (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).

bach-discml14.pdf (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).

barash-icwsm09.pdf (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).

bhattacharya-tkdd.pdf (346.13 KB)

Bhattacharya, I. & Getoor, L. Online Collective Entity Resolution. The 22nd National Conference on Artificial Intelligence (NECTAR Track) (AAAI Press, 2007).

nectar07.pdf (395.24 KB)

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

bhattacharya07a.pdf (309.63 KB)

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

bhattacharyasdm06.pdf (209.24 KB)

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

thesis.pdf (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).

kdd06.pdf (183.45 KB)

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

bhattacharyaicml06-wkshp.pdf (195.79 KB)

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

bhattacharyakdd05-whskp.pdf (259.82 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)

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).

bhattacharyasigmod04-wkshp.pdf (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).

acl04.pdf (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).

bilgic11a.pdf (1.64 MB)

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

bilgic-aaai10.pdf (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).

bilgic-icml10.pdf (515.65 KB)

Bilgic, M. Information Acquisition in Structured Domains. (2010).

mbilgic-phdthesis.pdf (4.68 MB)

Bilgic, M. & Getoor, L. Link-based Active Learning. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).

mbilgic-nips09wkshp.pdf (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).

bilgic-tkdd09.pdf (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).

bilgic-kdd08.pdf (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).

mgcs07.pdf (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).

bilgic-aaai07.pdf (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).

ddupe.pdf (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).

broecheler-nips10.pdf (382.51 KB)

Broecheler, M., Mihalkova, L. & Getoor, L. Probabilistic Similarity Logic. Conference on Uncertainty in Artificial Intelligence (2010).

broecheler-uai10.pdf (399.54 KB)

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

broecheler-srl09.pdf (176.13 KB)

C

Chajewska, U., Norman, J. & Getoor, L. Using Classication 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 Classication Problem. Uncertainty in Articial 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).

chang-sysml18.pdf (299.32 KB)

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