A credit assignment compiler for joint prediction

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 1713-1721

  Chang, Kai Wei ^a   He, He ^b   Daumé, Hal ^b   Langford, John ^c   Ross, Stephane ^d  

^a UNIVERSITY OF VIRGINIA   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

^c MICROSOFT RESEARCH   (United States)

^d GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; LEARNING SYSTEMS; PARALLEL PROCESSING SYSTEMS;

COMPLEX DECISION; CREDIT ASSIGNMENT; IMPERATIVE PROGRAMS; MACHINE LEARNING APPLICATIONS; MULTIPLE JOINTS; OUTPUT VARIABLES; PREDICTION TASKS; PROGRAMMING TIME;

PROGRAM COMPILERS;

EID: 85018863227     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (49)

1. A. Agarwal, O. Chapelle, M. Dudík, and J. Langford. A reliable effective terascale linear learning system. arXiv preprint arXiv:1110.4198, 2011.
2. D. Andor, C. Alberti, D. Weiss, A. Severyn, A. Presta, K. Ganchev, S. Petrov, and M. Collins. Globally normalized transition-based neural networks. Arxiv, 2016.
3. A. Beygelzimer, V. Dani, T. Hayes, J. Langford, and B. Zadrozny. Error limiting reductions between classification tasks. In ICML, pages 49-56, 2005.
4. L. Bottou. crfsgd project, 2011. http://leon.bottou.org/projects/sgd.
5. K.-W. Chang, A. Krishnamurthy, A. Agarwal, H. Daumé III, and J. Langford. Learning to search better than your teacher. In ICML, 2015.
6. K.-W. Chang, V. Srikumar, and D. Roth. Multi-core structural SVM training. In ECML, 2013.
7. K.-W. Chang, S. Upadhyay, M.-W. Chang, V. Srikumar, and D. Roth. Illinoissl: A JAVA library for structured prediction. Arxiv, 2015.
8. D. Chen and C. Manning. A fast and accurate dependency parser using neural networks. In EMNLP, pages 740-750, 2014.
9. M. Collins. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In EMNLP, 2002.
10. M. Collins and B. Roark. Incremental parsing with the perceptron algorithm. In ACL, 2004.
11. H. Daumé III, J. Langford, and D. Marcu. Search-based structured prediction. Machine Learning Journal, 2009.
12. H. Daumé III and D. Marcu. Learning as search optimization: Approximate large margin methods for structured prediction. In ICML, 2005.
13. J. R. Doppa, A. Fern, and P. Tadepalli. Output space search for structured prediction. In ICML, 2012.
14. J. R. Doppa, A. Fern, and P. Tadepalli. HC-Search: A learning framework for search-based structured prediction. JAIR, 50, 2014.
15. J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. JMLR, 12:2121-2159, 2011.
16. C. Dyer, M. Ballesteros, W. Ling, A. Matthews, and N. A. Smith. Transition-based dependency parsing with stack long short-term memory. In ACL, 2015.
17. J. Eisner, E. Goldlust, and N. A. Smith. Compiling comp ling: Practical weighted dynamic programming and the dyna language. In EMNLP, 2005.
18. U. Germann, M. Jahr, K. Knight, D. Marcu, and K. Yamada. Fast decoding and optimal decoding for machine translation. Artificial Intelligence, 154(1-2):127-143, 2003.
19. Y. Goldberg and J. Nivre. Training deterministic parsers with non-deterministic oracles. Transactions of the ACL, 1, 2013.
20. N. Goodman, V. Mansinghka, D. Roy, K. Bonawitz, and J. Tenenbaum. Church: a language for generative models. In UAI, 2008.
21. A. D. Gordon, T. A. Henzinger, A. V. Nori, and S. K. Rajamani. Probabilistic programming. In International Conference on Software Engineering (ICSE, FOSE track), 2014.
22. L. Huang, S. Fayong, and Y. Guo. Structured perceptron with inexact search. In NAACL, 2012.
23. T. Joachims, T. Finley, and C.-N. Yu. Cutting-plane training of structural SVMs. Machine Learning Journal, 2009.
24. N. Karampatziakis and J. Langford. Online importance weight aware updates. In UAI, 2011.
25. A. Kimmig, S. Bach, M. Broecheler, B. Huang, and L. Getoor. A short introduction to probabilistic soft logic. In NIPS Workshop on Probabilistic Programming, 2012.
26. P. Kordjamshidi, D. Roth, and H. Wu. Saul: Towards declarative learning based programming. In IJCAI, 2015.
27. T. Kudo. CRF++ project, 2005. http://crfpp.googlecode.com.
28. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, pages 282-289, 2001.
29. J. Langford, A. Strehl, and L. Li. Vowpal wabbit, 2007. http://hunch.net/~vw.
30. A. McCallum, D. Freitag, and F. Pereira. Maximum entropy Markov models for information extraction and segmentation. In ICML, 2000.
31. A. McCallum, K. Schultz, and S. Singh. FACTORIE: probabilistic programming via imperatively defined factor graphs. In NIPS, 2009.
32. B. Milch, B. Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov. BLOG: probabilistic models with unknown objects. Statistical relational learning, 2007.
33. T. Minka, J. Winn, J. Guiver, and D. Knowles. Infer.net 2.4, 2010. microsoft research cambridge, 2010.
34. A. Ng and M. Jordan. PEGASUS: A policy search method for large MDPs and POMDPs. In UAI, pages 406-415, 2000.
35. J. Nivre. An efficient algorithm for projective dependency parsing. In IWPT, pages 149-160, 2003.
36. A. Pfeffer. Ibal: A probabilistic rational programming language. In IJCAI, 2001.
37. L. Ratinov and D. Roth. Design challenges and misconceptions in named entity recognition. In CoNLL, 2009.
38. N. Ratliff, D. Bradley, J. A. Bagnell, and J. Chestnutt. Boosting structured prediction for imitation learning. In NIPS, 2007.
39. M. Richardson and P. Domingos. Markov logic networks. Machine learning, 62(1-2), 2006.
40. S. Ross and J. A. Bagnell. Reinforcement and imitation learning via interactive no-regret learning. arXiv:1406.5979, 2014.
41. S. Ross, G. J. Gordon, and J. A. Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In AI-Stats, 2011.
42. S. Ross, P. Mineiro, and J. Langford. Normalized online learning. In UAI, 2013.
43. D. Roth and S. W. Yih. Global inference for entity and relation identification via a linear programming formulation. In Introduction to Statistical Relational Learning. MIT Press, 2007.
44. W. M. Soon, H. T. Ng, and D. C. Y. Lim. A machine learning approach to coreference resolution of noun phrases. Computational Linguistics, 27(4):521-544, 2001.
45. U. Syed and R. E. Schapire. A reduction from apprenticeship learning to classification. In NIPS, 2011.
46. B. Taskar, C. Guestrin, and D. Koller. Max-margin Markov networks. In NIPS, 2003.
47. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In ICML, 2004.
48. Y. Xu and A. Fern. On learning linear ranking functions for beam search. In ICML, pages 1047-1054, 2007.
49. Y. Xu, A. Fern, and S. W. Yoon. Discriminative learning of beam-search heuristics for planning. In IJCAI, pages 2041-2046, 2007.