Archived Publications (Latest: https://linqs.github.io/linqs-website/publications/)

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2012
B. London, Huang, B., and Getoor, L., “Improved Generalization Bounds for Large-scale Structured Prediction”, in NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Networks, 2012.PDF icon london-nips12ws.pdf (213.95 KB)
2013
B. London, Huang, B., and Getoor, L., “Graph-based Generalization Bounds for Learning Binary Relations”. University of Maryland College Park, 2013.PDF icon br_risk_bounds.pdf (304.54 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.PDF icon bach-uai13.pdf (379.45 KB)
H. Miao, Liu, X., Huang, B., and Getoor, L., “A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization”, in 2013 IEEE International Conference on Big Data, 2013.PDF icon miao-bd13.pdf (307.51 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.PDF icon 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.PDF icon bach-inferning13.pdf (348.79 KB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., “Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic”, in NIPS Workshop on Data Driven Education, 2013.PDF icon ramesh-nipsws13.pdf (153.92 KB)
B. London, Rekatsinas, T., Huang, B., and Getoor, L., “Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss”. University of Maryland College Park, 2013.PDF icon mrwtd.pdf (460.45 KB)
B. London, Huang, B., Taskar, B., and Getoor, L., “PAC-Bayes Generalization Bounds for Randomized Structured Prediction”, in NIP Workshop on Perturbation, Optimization and Statistics, 2013.PDF icon london-nips13ws.pdf (205.57 KB)
2014
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)
D. Sridhar, Foulds, J., Huang, B., Walker, M., and Getoor, L., “Collective classification of stance and disagreement in online debate forums”, in Bay Area Machine Learning Symposium (BayLearn), 2014.
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., “Learning Latent Engagement Patterns of Students in Online Courses”, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.PDF icon ramesh-aaai14.pdf (505.47 KB)
S. Fakhraei, Huang, B., Raschid, L., and Getoor, L., “Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014.PDF icon fakhraei-tcbb2014_accepted.pdf (3.97 MB)
B. London, Huang, B., Taskar, B., and Getoor, L., “PAC-Bayesian Collective Stability”, in Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, 2014.PDF icon london-aistats14.pdf (490.14 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.PDF icon bach-dssg14.pdf (124.88 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.PDF icon bach-discml14.pdf (254.9 KB)
B. London, Huang, B., and Getoor, L., “On the Strong Convexity of Variational Inference”, in NIPS Workshop on Advances in Variational Inference, 2014.PDF icon london-nips14ws.pdf (253.72 KB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., “Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs”, in ACM Conference on Learning at Scale, 2014.
2015
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.PDF icon bach-arxiv15.pdf (686.27 KB)
D. Sridhar, Foulds, J., Walker, M., Huang, B., and Getoor, L., “Joint Models of Disagreement and Stance in Online Debate”, in Annual Meeting of the Association for Computational Linguistics (ACL), 2015.PDF icon sridhar-acl15.pdf (227.14 KB)
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.PDF icon bach-icml15.pdf (356.46 KB)
B. London, Huang, B., and Getoor, L., “Stability and Generalization in Structured Prediction”, –, 2015.PDF icon london-stability15.pdf (532.16 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.PDF icon bach-aistats15.pdf (345.2 KB)
2017
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.PDF icon bach-jmlr17.pdf (731.56 KB)