Learning from labeled features using generalized expectation criteria G Druck, G Mann, A McCallum Proceedings of the 31st annual international ACM SIGIR conference on …, 2008 | 333 | 2008 |
Active learning by labeling features G Druck, B Settles, A McCallum Proceedings of the 2009 conference on Empirical methods in natural language …, 2009 | 222 | 2009 |
Multi-conditional learning: Generative/discriminative training for clustering and classification A McCallum, C Pal, G Druck, X Wang AAAI 1, 6, 2006 | 138 | 2006 |
Semi-supervised classification with hybrid generative/discriminative methods G Druck, C Pal, A McCallum, X Zhu Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007 | 104 | 2007 |
Alternating projections for learning with expectation constraints K Bellare, G Druck, A McCallum arXiv preprint arXiv:1205.2660, 2012 | 74 | 2012 |
Learning to predict the quality of contributions to wikipedia G Druck, G Miklau, A McCallum WikiAI 8, 7-12, 2008 | 73 | 2008 |
Semi-supervised learning of dependency parsers using generalized expectation criteria G Druck, G Mann, A McCallum Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL …, 2009 | 59 | 2009 |
Generalized expectation criteria A McCallum, G Mann, G Druck Computer science technical note, University of Massachusetts, Amherst, MA 94 …, 2007 | 50 | 2007 |
High-performance semi-supervised learning using discriminatively constrained generative models G Druck, A McCallum Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 41 | 2010 |
Spice it up? Mining refinements to online instructions from user generated content G Druck, B Pang Proceedings of the 50th Annual Meeting of the Association for Computational …, 2012 | 26 | 2012 |
Toward interactive training and evaluation G Druck, A McCallum Proceedings of the 20th ACM international conference on Information and …, 2011 | 26 | 2011 |
Generalized expectation criteria for lightly supervised learning G Druck | 20 | 2011 |
Leveraging existing resources using generalized expectation criteria G Druck, G Mann, A McCallum Proceedings of NIPS Workshop on Learning Problem Design, 2007 | 9 | 2007 |
Reducing annotation effort using generalized expectation criteria G Druck, G Mann, A McCallum Technical Report 2007-62, University of Massachusetts, Amherst, 2007 | 7 | 2007 |
Computing conditional feature covariance in non-projective tree conditional random fields G Druck, D Smith Technical Report UM-CS-2009-060, University of Massachusetts, 2009 | 3 | 2009 |
On discriminative and semi-supervised dimensionality reduction C Pal, M Kelm, X Wang, G Druck, A McCallum Workshop on Novel Applications of Dimensionality Reduction, NIPS 6, 2006 | 3 | 2006 |
Rich prior knowledge in learning for natural language processing G Druck, K Ganchev, J Graça Proceedings of the 49th Annual Meeting of the Association for Computational …, 2011 | 2 | 2011 |
Learning A* priority function from unlabeled data M Narasimhan, PA Viola, G Druck US Patent 7,840,503, 2010 | 2 | 2010 |
Learning A* underestimates: Using inference to guide inference G Druck, M Narasimhan, P Viola Artificial Intelligence and Statistics, 99-106, 2007 | 2 | 2007 |
Rich prior knowledge in learning for NLP G Druck, K Ganchev, J Graça Proceedings of the 49th Annual Meeting of the Association for Computational …, 2011 | 1 | 2011 |