Export 24 results:
Author Title [ Year(Asc)]
Filters: Author is Huang, Bert  [Clear All Filters]
Ramakrishnan, N. et al. ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2014).PDF icon ramakrishnan-kdd14.pdf (1.15 MB)
Sridhar, D., Foulds, J., Huang, B., Walker, M. & Getoor, L. Collective classification of stance and disagreement in online debate forums. Bay Area Machine Learning Symposium (BayLearn) (2014).
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Learning Latent Engagement Patterns of Students in Online Courses. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI Press, 2014).PDF icon ramesh-aaai14.pdf (505.47 KB)
Fakhraei, S., Huang, B., Raschid, L. & 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)
London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayesian Collective Stability. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (2014).PDF icon london-aistats14.pdf (490.14 KB)
Bach, S. H., Huang, B. & Getoor, L. Probabilistic Soft Logic for Social Good. KDD Workshop on Data Science for Social Good (2014).PDF icon bach-dssg14.pdf (124.88 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).PDF icon bach-discml14.pdf (254.9 KB)
London, B., Huang, B. & Getoor, L. On the Strong Convexity of Variational Inference. NIPS Workshop on Advances in Variational Inference (2014).PDF icon london-nips14ws.pdf (253.72 KB)
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs. ACM Conference on Learning at Scale (ACM, 2014).
London, B., Huang, B. & Getoor, L. Graph-based Generalization Bounds for Learning Binary Relations. (2013).PDF icon br_risk_bounds.pdf (304.54 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).PDF icon bach-uai13.pdf (379.45 KB)
Miao, H., Liu, X., Huang, B. & Getoor, L. A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization. 2013 IEEE International Conference on Big Data (2013).PDF icon miao-bd13.pdf (307.51 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).PDF icon 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).PDF icon bach-inferning13.pdf (348.79 KB)
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic. NIPS Workshop on Data Driven Education (2013).PDF icon ramesh-nipsws13.pdf (153.92 KB)
London, B., Rekatsinas, T., Huang, B. & Getoor, L. Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss. (2013).PDF icon mrwtd.pdf (460.45 KB)
London, B., Huang, B., Taskar, B. & Getoor, L. PAC-Bayes Generalization Bounds for Randomized Structured Prediction. NIP Workshop on Perturbation, Optimization and Statistics (2013).PDF icon london-nips13ws.pdf (205.57 KB)
London, B., Huang, B. & Getoor, L. Improved Generalization Bounds for Large-scale Structured Prediction. NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Networks (2012).PDF icon london-nips12ws.pdf (213.95 KB)