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2019

Embar, V., Pujara, J. & Getoor, L. Collective Alignment of Large-scale Ontologies. AKBC Workshop on Federated KBs and the Open Knowledge Network (2019).

embar-akbc19.pdf (47.25 KB)

Ramesh, A., Goldwasser, D., Huang, B., Daume-III, H. & Getoor, L. Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields. Transactions on Learning Technologies (2019).

ramesh-tlt19.pdf (4.3 MB)

Srinivasan, S., Babaki, B., Farnadi, G. & Getoor, L. Lifted Hinge-Loss Markov Random Fields. 33rd AAAI Conference on Artificial Intelligence (2019).

srinivasan-aaai19.pdf (417.5 KB)

Kouki, P., Schaffer, J., Pujara, J., ODonovan, J. & Getoor, L. Personalized Explanations for Hybrid Recommender Systems. Intelligent User Interfaces (2019).

kouki-iui19.pdf (3.34 MB)

Embar, V., Srinivasan, S. & Getoor, L. Tractable Marginal Inference for Hinge-Loss Markov Random Fields. ICML TPM (2019) (2019).

Augustine, E., Rekatsinas, T. & Getoor, L. Tractable Probabilistic Reasoning Through Effective Grounding. ICML TPM (2019).

2018

Embar, V., Farnadi, G., Pujara, J. & Getoor, L. Aligning Product Categories using Anchor Products. First Workshop on Knowledge Base Construction, Reasoning and Mining (2018).

embar-kbcom18.pdf (577.65 KB)

Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018).

chang-sysml18.pdf (299.32 KB)

Kouki, P., Pujra, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Multi-Relational Familial Networks. Knowledge and Information Systems (KAIS) (2018).

kouki-kais18.pdf (1.17 MB)

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)

Sridhar, D., Springer, A., Hollis, V., Whittaker, S. & Getoor, L. Estimating Causal Effects of Exercise from Mood Logging Data. ICML Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (2018).

sridhar-causalml18.pdf (333.69 KB)

Farnadi, G., Babaki, B. & Getoor, L. Fairness in Relational Domains. AAAI/ACM Conference on AI, Ethics, and Society (2018).

farnadi_aies2018.pdf (418.24 KB)

Farnadi, G., Kouki, P., Thompson, S. K., Srinivasan, S. & Getoor, L. A Fairness-aware Hybrid Recommender System. The 2nd FATREC Workshop on Responsible Recommendation (2018).

GFarnadi-FATREC2018.pdf (474.04 KB)

Farnadi, G., Babaki, B. & Getoor, L. Fairness-aware Relational Learning and Inference. Third International Workshop on Declarative Learning Based Programming (DeLBP) at thirty-second AAAI conference on Artificial Intelligence (2018).

GFarnadi-DeLBP2018.pdf (169.37 KB)

Tomkins, S., Farnadi, G., Amantullah, B., Getoor, L. & Minton, S. The Impact of Environmental Stressors on Human Trafficking. Beyond Online Data (ICWSM Workshop) (2018).

icdm_2018.pdf (473.58 KB)

Tomkins, S., Farnadi, G., Amantullah, B., Getoor, L. & Minton, S. The Impact of Environmental Stressors on Human Trafficking. International Conference on Data Mining (ICDM) (2018).

icdm_2018.pdf (473.58 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)

Kouki, P. Resolution, Recommendation, and Explanation in Richly Structured Social Networks. Technology and Information Management Ph.D. Thesis, (2018).

kouki-dissertation.pdf (6.94 MB)

Sridhar, D., Pujara, J. & Getoor, L. Scalable Probabilistic Causal Structure Discovery. International Joint Conference on Artificial Intelligence (2018). at <https://bitbucket.org/linqs/causpsl/src/master/>

sridhar-ijcai18.pdf (281.32 KB)

Embar, V., Sridhar, D., Farnadi, G. & Getoor, L. Scalable Structure Learning for Probabilistic Soft Logic. Workshop on Statistical Relational AI (2018).

VEmbar-StarAI2018.pdf (400.23 KB)

Tomkins, S., Getoor, L., Chen, Y. & Zhang, Y. A Socio-linguistic Model for Cyberbullying Detection. International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018).

tomkins-asonam18.pdf (299.34 KB)

Zhang, Y., Ramesh, A., Golbeck, J., Sridhar, D. & Getoor, L. A Structured Approach to Understanding Recovery and Relapse in AA. The Web Conference (WWW) (2018). at <https://github.com/yzhan202/zhang-www18-experiments>

zhang-www18.pdf (800.66 KB)

Tomkins, S., Isley, S., London, B. & Getoor, L. Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations. Recommender Systems (RecSys) (2018).

recsys_2018.pdf (655.92 KB)

Ramesh, A. & Getoor, L. Understanding Evolution of Long-running MOOCs. International Conference on Web Information Systems Engineering (WISE) (2018).

2017

Kouki, P., Pujara, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Familial Networks. IEEE International Conference on Data Mining (ICDM) (2017). at <https://github.com/pkouki/icdm2017>

kouki-icdm17.pdf (653.4 KB)

Kimmig, A., Memory, A., Miller, R. & Getoor, L. A Collective, Probabilistic Approach to Schema Mapping. International Conference on Data Engineering (ICDE) (2017). at <https://github.com/alexmemory/kimmig-icde17/wiki>

kimmig-icde17.pdf (463.69 KB)

Tomkins, S., Getoor, L., Chen, Y. & Zhang, Y. Detecting Cyber-bullying from Sparse Data and Inconsistent Labels. Learning with Limited Labeled Data (LLD) NIPS Workshop (2017).

tomkins-NIPSLLD17.pdf (286.95 KB)

Tomkins, S., Pujara, J. & Getoor, L. Disambiguating Energy Disaggregation: A Collective Probabilistic Approach. International Joint Conference on Artificial Intelligence (2017).

tomkins-ijcai17.pdf (373.28 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)

Ramesh, A., Rodriguez, M. & Getoor, L. Multi-relational influence models for online professional networks. International Conference on Web Intelligence (ICWI) 291-298 (ACM, 2017).

ramesh-icwi17.pdf (761.17 KB)

Kim, S., Kini, N., Pujara, J., Koh, E. & Getoor, L. Probabilistic Visitor Stitching on Cross-Device Web Logs. International Conference on World Wide Web (WWW) 1581–1589 (2017).

p1581-kimwww17.pdf (1.23 MB)

Farnadi, G., Bach, S. H., Moens, M. - F., Getoor, L. & De Cock, M. Soft quantiﬁcation in statistical relational learning. Machine Learning Journal (2017).

farnadi-mlj17.pdf (1.24 MB)

Pujara, J., Augustine, E. & Getoor, L. Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short. Conference on Empirical Methods in Natural Language Processing (EMNLP) (2017). at <https://github.com/eriq-augustine/meta-kg>

pujara-emnlp17.pdf (677.74 KB)

Kouki, P., Schaffer, J., Pujara, J., ODonovan, J. & Getoor, L. User Preferences for Hybrid Explanations. 11th ACM Conference on Recommender Systems (RecSys) (2017).

kouki-recsys17.pdf (2.64 MB)

Sridhar, D., Pujara, J. & Getoor, L. Using Noisy Extractions to Discover Causal Knowledge. NIPS Workshop on Automated Knowledge Base Construction (2017).

sridhar-akbc17.pdf (203.34 KB)

2016

Fakhraei, S., Sridhar, D., Pujara, J. & Getoor, L. Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. 12th International SIGKDD Workshop on Mining and Learning with Graphs (MLG) (ACM SIGKDD, 2016).

fakhraei_mlg_2016.pdf (711.26 KB)

Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Magazine 37, 63–75 (2016).

muthiah-aimag16.pdf (1.23 MB)

Kouki, P., Marcum, C., Koehly, L. & Getoor, L. Entity Resolution in Familial Networks. 12th International Workshop on Mining and Learning with Graphs (2016).

kouki-mlg16.pdf (633.08 KB)

Rekatsinas, T. et al. Forecasting Rare Disease Outbreaks from Open Source Indicators. Statistical Analysis and Data Mining: The ASA Data Science Journal (2016).

rekatsinas-sadm17.pdf (303.08 KB)

Pujara, J. & Getoor, L. Generic Statistical Relational Entity Resolution in Knowledge Graphs. Sixth International Workshop on Statistical Relational AI (IJCAI 2016, 2016).

pujara-starai16.pdf (151.37 KB)

Sridhar, D. & Getoor, L. Joint Probabilistic Inference of Causal Structure. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop on Causal Discovery (2016).

sridhar-kdd-ws16.pdf (204.51 KB)

Tomkins, S., Ramesh, A. & Getoor, L. Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study. International Conference on Educational Data Mining (EDM) (2016).

tomkins-edm16.pdf (619.77 KB)

Sridhar, D., Fakhraei, S. & Getoor, L. A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction. Bioinformatics (2016).

sridhar-bioinformatics_2016.pdf (1.94 MB)

Ramesh, A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. (2016).

ramesh-thesis16.pdf (865.41 KB)

Sridhar, D. & Getoor, L. Probabilistic Inference for Causal Structure Discovery. Uncertainty in Artificial Intelligence (UAI) Workshop on Causation (2016).

sridhar-uai-open-problem-v2.pdf (118.31 KB)

Pujara, J. Probabilistic Models for Scalable Knowledge Graph Construction. (2016).

pujara-thesis15.pdf (1.06 MB)

Rekatsinas, T., Deshpande, A., Dong, X. Luna, Getoor, L. & Srivastava, D. SourceSight: Enabling Effective Source Selection. ACM SIGMOD Conference (2016).

modde087.pdf (799.94 KB)

London, B., Huang, B. & Getoor, L. Stability and Generalization in Structured Prediction. Journal of Machine Learning Research 17, (2016).

london-jlmr17.pdf (532.8 KB)

Kumar, S. et al. Unsupervised Models for Predicting Strategic Relations between Organizations. IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE, 2016).

kumar_asonam16.pdf (212.61 KB)

2015

London, B., Huang, B. & Getoor, L. The Benefits of Learning with Strongly Convex Approximate Inference. International Conference on Machine Learning (ICML) (2015).

london-icml15.pdf (788.06 KB)

Pages