Structured prediction is a framework for solving problems of classification or regression in which the output variables are mutually dependent or constrained. These dependencies and constraints reflect sequential, spatial or combinatorial structure in the problem domain, and capturing such interactions is often as important as capturing input-output dependencies. Many such problems, including natural language parsing, machine translation, object segmentation, gene prediction, protein alignment and numerous other tasks in computational linguistics, speech, vision, biology, are not new. However, recent advances have brought about a unified view, efficient methodology and more importantly, significant accuracy improvements for both classical and novel problems. Research in structured prediction explores the fundamental computational and statistical challenges arising from the high dimensionality of the inputs and the exponential explosion of the number of possible joint outcomes.
A list of recent papers:
An End-to-End Discriminative Approach to Machine Translation, P. Liang, Alexandre Bouchard-Cote, D. Klein and B. Taskar. Association for Computational Linguistics (ACL06), Sydney, Australia, July 2006.
Word Alignment via Quadratic Assignment, S. Lacoste-Julien, B. Taskar, D. Klein, and M. Jordan. Human Language Technology conference - North American chapter of the Association for Computational Linguistics (HLT-NAACL06), New York, June 2006.
Structured Prediction, Dual Extragradient and Bregman Projections, B. Taskar, S. Lacoste-Julien, and M. Jordan. Journal of Machine Learning Research (JMLR), Special Topic on Machine Learning and Large Scale Optimization.
Structured Prediction via the Extragradient Method, B. Taskar, S. Lacoste-Julien, and M. Jordan, Neural Information Processing Systems Conference (NIPS05), Vancouver, British Columbia, December 2005.
A Discriminative Matching Approach to Word Alignment, B. Taskar, S. Lacoste-Julien, and D. Klein, Empirical Methods in Natural Language Processing (EMNLP05), Vancouver, British Columbia, October 2005.
Tutorial: Max-Margin Methods for NLP: Estimation, Structure, and Applications. The Association for Computational Linguistics (ACL05), Ann Arbor, MI, June 2005.
Learning Structured Prediction Models: A Large Margin Approach. B. Taskar, V. Chatalbashev, D. Koller and C. Guestrin. Twenty Second International Conference on Machine Learning (ICML05), Bonn, Germany, August 2005.
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data. D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, A. Ng. International Conference on Computer Vision and Pattern Recognition (CVPR05), San Diego, CA, June 2005.
Thesis: Learning Structured Prediction Models: A Large Margin Approach. Stanford University, CA, December 2004.
Exponentiated gradient algorithms for large-margin structured classification, P. Bartlett, M. Collins, B. Taskar and D. McAllester. Neural Information Processing Systems Conference (NIPS04), Vancouver, Canada, December 2004.
Max-Margin Parsing, B. Taskar, D. Klein, M. Collins, D. Koller and C. Manning. Empirical Methods in Natural Language Processing (EMNLP04), Barcelona, Spain, July 2004. Received best paper award.
Learning Associative Markov Networks, B. Taskar, V. Chatalbashev and D. Koller. Twenty First International Conference on Machine Learning (ICML04), Banff, Canada, July 2004.
Max-Margin Markov Networks, B. Taskar, C. Guestrin and D. Koller. Neural Information Processing Systems Conference (NIPS03), Vancouver, Canada, December 2003.