Kevin Small Amazon.com Core Machine Learning Machine Learning Scientist

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This page contains all of my publications, of which most can also be found through my Google Scholar page. I also have a few BibTeX files that you may find useful. 1For my biomedical research friends, please be aware that conference publications are the primary vehicle for disseminating research results to the computer science community. *Authors listed in alphabetical order. Journal Articles1 C. E. Brodley, U. Rebbapragada, K. Small*, and B. C. Wallace, Challenges and Opportunities in Applied Machine Learning. Artificial Intelligence Magazine, 33(1):11-24, 2012. B. C. Wallace, K. Small, C. E. Brodley, J. Lau, C. H. Schmid, L. Bertram, M. Lill, J. T. Cohen, and T. A. Trikalinos, Towards Modernizing the Systematic Review Pipeline in Genetics: Updating via Data Mining. Genetics in Medicine, 13:663-669, 2012. K. Small and D. Roth, Margin-based Active Learning for Structured Predictions. International Journal of Machine Learning and Cybernetics, 1(1-4):3-25, 2010. Conference Papers1 B. Wallace, K. Small, C. Brodley, J. Lau, and T. Trikalinos, Deploying an Interactive Machine Learning System in an Evidence-Based Practice Center. In Proc. of the ACM International Health Informatics Symposium (IHI), 2012. B. Wallace, K. Small, C. Brodley, and T. Trikalinos, Class Imbalance, Redux. In Proc. of the International Conference on Data Mining (ICDM), 2011. K. Small, B. Wallace, C. Brodley, and T. Trikalinos, The Constrained Weight Space SVM: Learning with Ranked Features. In Proc. of the International Conference on Machine Learning (ICML), 2011. B. Wallace, K. Small, C. Brodley, and T. Trikalinos, Who Should Label What? Instance Allocation in Multiple Expert Active Learning. In Proc. of the SIAM International Conference on Data Mining (SDM), 2011. B. Wallace, K. Small, C. Brodley, J. Lau, and T. Trikalinos, Modeling Annotation Time to Reduce Workload in Comparative Effectiveness Reviews. In Proc. of the ACM International Health Informatics Symposium (IHI 2010), 2010. B. Wallace, K. Small, C. Brodley, and T. Trikalinos, Active Learning for Biomedical Citation Screening. In Proc. of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010. I. Titov, A. Klementiev, K. Small, and D. Roth, Unsupervised Aggregation for Classification Problems with Large Numbers of Categories. In Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2010. D. Roth and K. Small*, Interactive Feature Space Construction using Semantic Information. In Proc. of the Conference on Computational Natural Language Learning (CoNLL), 2009. A. Klementiev, D. Roth, K. Small*, and I. Titov, Unsupervised Rank Aggregation with Domain-Specific Expertise. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 2009. D. Roth, K. Small*, and I. Titov, Sequential Learning of Classifiers for Structured Prediction Problems. In Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. A. Klementiev, D. Roth, and K. Small*, Unsupervised Rank Aggregation with Distance-based Models. In Proc. of the International Conferece on Machine Learning (ICML), 2008. D. Roth and K. Small*, Active Learning for Pipeline Models. In Proc. of the National Conference on Artificial Intelligence (AAAI), 2008. A. Klementiev, D. Roth, and K. Small*, An Unsupervsied Learning Algorithm for Rank Aggregation. In Proc. of the European Conference on Machine Learning (ECML), 2007. P. Davis, K. Small*, and Z. Xie, All Links are Not the Same: Evaluating Word Alignments for Statistical Machine Translation. In Proc. of the Machine Translation Summit (MT Summit), 2007. D. Roth and K. Small*, Margin-based Active Learning for Structured Output Spaces. In Proc. of the European Conference on Machine Learning (ECML), 2006. X. Li, D. Roth, and K. Small*, The Role of Semantic Information in Learning Question Classifiers. In Proc. of the International Joint Conference on Natural Language Processing (IJCNLP), 2004. C. Cumby, X. Li, P. Morie, R. Nagarajan, N. Rizzolo, D. Roth, K. Small*, and W. Yih, Question Answering via Enhanced Understanding of Questions. In Proc. of the Text Retreival Conference (TREC), 2002. Workshops and Symposia K. Pham, N. Rizzolo, K. Small, K. Chang, and D. Roth, Object Search: Supporting Structured Queries in Web Search Engines, NAACL Workshop on Semantic Search (SemanticSearch), 2010. B. Wallace, K. Small, C. Brodley, and T. Trikalinos, Active Learning for Biomedical Citation Screening Northeast Student Colloquium on Artificial Intelligence, 2010. K. Small and D. Roth, Interactive Feature Space Construction, NIPS Workshop on Analysis and Design of Algorithms for Interactive Machine Learning (ADA-IML), 2009. A. Klementiev, D. Roth, K. Small*, and I. Titov, Unsupervised Prediction Aggregation. NIPS Workshop on Learning with Orderings, 2009. A. Klementiev, D. Roth, K. Small*, and I. Titov, Unsupervised Rank Aggregation with Domain-Specific Expertise. The Learning Workshop (Snowbird), 2009. D. Roth and K. Small*, Interactive Introduction of Semantic Information for Discriminative Learning. NSF Symposium on Semantic Knowledge Discovery, Organization, and Use, 2008. A. Klementiev, D. Roth, and K. Small*, A Framework for Unsupervised Rank Aggregation. SIGIR-08 Workshop on Learning to Rank for Information Retrieval (LR4IR), 2008. D. Roth and K. Small*, Active Learning for Pipeline Models, The Learning Workshop (Snowbird), 2008. A. Klementiev, D. Roth, and K. Small*, Unsupervised Rank Aggregation with Distance-based Models. The Learning Workshop (Snowbird), 2008. D. Roth and K. Small*, Active Learning with Perceptron for Structured Output. ICML-06 Workshop on Learning in Structured Output Spaces, 2006.