Online max-margin weight learning for Markov logic networks

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 642-651

  Huynh, Tuyen N ^a   Mooney, Raymond J ^a  

^a University of Texas at Austin   (United States)

Author keywords

Online learning; Statistical relational learning; Structured prediction

Indexed keywords

COMPUTER CIRCUITS; DATA MINING; E-LEARNING; LARGE DATASET; MARKOV PROCESSES; PROBABILISTIC LOGICS; SPACE DIVISION MULTIPLE ACCESS;

MARKOV LOGIC NETWORKS; ON-LINE ALGORITHMS; ONLINE LEARNING; ONLINE LEARNING ALGORITHMS; REAL-WORLD DATASETS; STATISTICAL RELATIONAL LEARNING; STRUCTURED PREDICTION; TRAINING EXAMPLE;

LEARNING ALGORITHMS;

EID: 80052396679     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.55     Document Type: Conference Paper

References (42)

1. Bakir, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N. (eds.): Predicting Structured Data. The MIT Press (2007)
2. Bartlett, P.L., Collins, M., Taskar, B., McAllester, D.A.: Exponentiated gradient algorithms for large-margin structured classification. In: Advances in Neural Information Processing Systems 17, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada (2005)
3. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)
4. Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 shared task: Semantic role labeling. In: Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005). pp. 152-164. Ann Arbor, MI (Jun 2005)
5. Collins, M.: Discriminative training methods for hidden Markov models: Theory and experiments with per-ceptron algorithms. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP-02). Philadelphia, PA (Jul 2002)
6. Collins, M., Globerson, A., Koo, T., Carreras, X., Bartlett, P.L.: Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks. Journal of Machine Learning Research 9, 1775-1822 (2008)
7. Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. Journal of Machine Learning Research 7, 551-585 (2006)
8. Culotta, A.: Learning and inference in weighted logic with application to natural language processing. Ph.D. thesis, University of Massachusetts (2008)
9. Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10(7), 1895-1923 (1998)
10. Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers (2009)
11. Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge, MA (2007)
12. Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Computation 14(8), 1771-1800 (2002)
13. Huynh, T.N., Mooney, R.J.: Discriminative structure and parameter learning for Markov logic networks. In: Proceedings of the 25th International Conference on Machine Learning (ICML-2008). pp. 416-423. Helsinki, Finland (2008)
14. Huynh, T.N., Mooney, R.J.: Max-margin weight learning for Markov logic networks. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2009), Part I. pp. 564-579 (2009)
15. Joachims, T.: A support vector method for multivariate performance measures. In: Proceedings of 22nd International Conference on Machine Learning (ICML-2005). pp. 377-384 (2005)
16. Kakade, S.M., Shalev-Shwartz, S.: Mind the duality gap: Logarithmic regret algorithms for online optimization. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 21, Vancouver, British Columbia, Canada, December 8-11, 2008. pp. 1457-1464. MIT Press (2009)
17. Kautz, H., Selman, B., Jiang, Y.: A general stochastic approach to solving problems with hard and soft constraints. In: Dingzhu Gu, J.D., Pardalos, P. (eds.) The Satisfiability Problem: Theory and Applications. pp. 573-586. American Mathematical Society (1997)
18. Kok, S., Domingos, P.: Learning Markov logic networks using structural motifs. In: Fürnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning (ICML-10). pp. 551-558. Haifa, Israel (June 2010)
19. Lawrence, S., Giles, C.L., Bollacker, K.D.: Autonomous citation matching. In: Proceedings of the Third Annual Conference on Autonomous Agents (1999)
20. Lowd, D., Domingos, P.: Efficient weight learning for Markov logic networks. In: Proceedings of 7th European Conference of Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-2007). pp. 200-211 (2007)
21. McCallum, A., Schultz, K., Singh, S.: FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs. In: Advances in Neural Information Processing Systems 22 (NIPS-2009). pp. 1249-1257 (2009)
22. McDonald, R., Crammer, K., Pereira, F.: Online large-margin training of dependency parsers. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL-05). pp. 91-98. Association for Computational Linguistics, Morristown, NJ, USA (2005)
23. Mihalkova, L., Mooney, R.J.: Bottom-up learning of Markov logic network structure. In: Proceedings of 24th International Conference on Machine Learning (ICML-2007). Corvallis, OR (June 2007)
24. Mihalkova, L., Mooney, R.J.: Learning to disam-biguate search queries from short sessions. In: Buntine, W.L., Grobelnik, M., Mladenic, D., Shawe-Taylor, J. (eds.) Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2009), Part II. pp. 111-127 (2009)
25. Nathan Ratliff, J.A.D.B., Zinkevich, M.: (Online) subgradient methods for structured prediction. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AIStats) (2007)
26. Palmer, M., Gildea, D., Kingsbury, P.: The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics 31(1), 71-106 (2005)
27. Poon, H., Domingos, P.: Sound and efficient inference with probabilistic and deterministic dependencies. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06). Boston, MA (July 2006)
28. Poon, H., Domingos, P.: Joint inference in information extraction. In: Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07). pp. 913-918. Vancouver, British Columbia, Canada (2007)
29. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107-136 (2006)
30. Riedel, S.: Improving the accuracy and efficiency of MAP inference for Markov logic. In: Proceedings of 24th Conference on Uncertainty in Artificial Intelligence (UAI-2008). pp. 468-475. Helsinki, Finland (2008)
31. Riedel, S., Meza-Ruiz, I.: Collective semantic role labelling with Markov logic. In: Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL'08) (2008)
32. Shalev-Shwartz, S.: Online Learning: Theory, Algorithms, and Applications. Ph.D. thesis, The Hebrew University of Jerusalem (2007)
33. Shalev-Shwartz, S., Singer, Y.: Convex repeated games and Fenchel duality. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 1265-1272. MIT Press (2007)
34. Shalev-Shwartz, S., Singer, Y.: A unified algorithmic approach for efficient online label ranking. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AIStats) (2007)
35. Singla, P., Domingos, P.: Discriminative training of Markov logic networks. In: Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05). pp. 868-873 (2005)
36. Singla, P., Domingos, P.: Lifted first-order belief propagation. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-08). pp. 1094-1099. Chicago, Illinois, USA (2008)
37. Snow, R., O'Connor, B., Jurafsky, D., Ng, A.Y.: Cheap and fast-but is it good?: evaluating nonexpert annotations for natural language tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2008). pp. 254-263. Association for Computational Linguistics, Morristown, NJ, USA (2008)
38. Taskar, B., Chatalbashev, V., Koller, D., Guestrin, C: Learning structured prediction models: a large margin approach. In: Proceedings of 22nd International Conference on Machine Learning (ICML-2005). pp. 896-903. ACM, Bonn, Germany (2005)
39. Taskar, B., Guestrin, C, Koller, D.: Max-margin Markov networks. In: Advances in Neural Information Processing Systems 16 (NIPS 2003) (2003)
40. Taskar, B., Abbeel, P., Koller, D.: Discriminative probabilistic models for relational data. In: Proceedings of 18th Conference on Uncertainty in Artificial Intelligence (UAI-2002). pp. 485-492. Edmonton, Canada (2002)
41. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: Proceedings of 21st International Conference on Machine Learning (ICML-2004). pp. 104-112. Banff, Canada (July 2004)
42. Zinkevich, M.: Online Convex Programming and Generalized Infinitesimal Gradient Ascent. In: Proceedings of 20th International Conference on Machine Learning (ICML-2003). pp. 928-936 (2003)