Factorial multi-task learning: A Bayesian nonparametric approach

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 2, 2013, Pages 1694-1702

  Gupta, Sunil Kumar ^a   Phung, Dinh ^a   Venkatesh, Svetha ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

DIRICHLET PROCESS; INFINITE NUMBERS; JOINT LEARNING; MULTITASK LEARNING; NON-PARAMETRIC; NONPARAMETRIC APPROACHES; PERFORMANCE DEGRADATION; TASK RELATEDNESS;

LEARNING SYSTEMS;

EID: 84897497498     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Agarwal, A., Daumé III, H., and Gerber, S. Learning multiple tasks using manifold regularization. Advances in neural information processing systems, 23:46-54, 2010.
2. Ando, R.K. and Zhang, T. A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research, 6: 1817-1853, 2005.
3. Argyriou, A., Evgeniou, T., and Pontil, M. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
4. Baxter, J. A model of inductive bias learning. Journal of Artificial Intelligence Research, 12:149-198, 2000.
5. Bi, J., Xiong, T., Yu, S., Dundar, M., and Rao, R. An improved multi-task learning approach with applications in medical diagnosis. Machine Learning and Knowledge Discovery in Databases, pp. 117-132, 2008.
6. Bishop, Christopher M et al. Pattern recognition and machine learning, volume 4. springer New York, 2006.
7. Caruana, R. Multitask learning. Machine Learning, 28(1): 41-75, 1997. (Pubitemid 127507169)
8. Corduneanu, A. and Bishop, C.M. Variational bayesian model selection for mixture distributions. In Artificial intelligence and Statistics, volume 2001, pp. 27-34. Morgan Kaufmann Waltham, MA, 2001.
9. Daumé III, H. Bayesian multitask learning with latent hierarchies. In Proc. of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 135-142, 2009.
10. Ferguson, T.S. A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2):209-230, 1973.
11. Gilks, Walter R and Wild, Pascal. Adaptive rejection sampling for gibbs sampling. Applied Statistics, pp. 337-348, 1992.
12. Gilks, W.R., Richardson, S., and Spiegelhalter, D. Markov Chain Monte Carlo in practice: interdisciplinary statistics, volume 2. Chapman & Hall/CRC, 1995.
13. Gupta, S.K., Phung, D., Adams, B., and Venkatesh, S. A Bayesian framework for learning shared and individual subspaces from multiple data sources. In Advances in Knowledge Discovery and Data Mining, 15th Pacific-Asia Conference, pp. 136-147, 2011.
14. Gupta, S.K., Phung, D., and Venkatesh, S. A Bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources. In Proceedings of 12th SIAM International Conference on Data Mining, pp. 200-211, 2012a.
15. Gupta, S.K., Phung, D., and Venkatesh, S. A slice sampler for restricted hierarchical beta process with applications to shared subspace learning. In Proc. of 28th Uncertainty in Artificial Intelligence (UAI), pp. 316-325, 2012b.
16. Gupta, S.K., Phung, D., Adams, B., and Venkatesh, S. Regularized nonnegative shared subspace learning. Data Mining and Knowledge Discovery, 26(1):57-97, 2013.
17. Kang, Z., Grauman, K., and Sha, F. Learning with whom to share in multi-task feature learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 521-528, 2011.
18. Kingman, J.F.C. Completely random measures. Pacific Journal of Mathematics, 21(1):59-78, 1967.
19. Kumar, A. and Daumé III, H. Learning task grouping and overlap in multi-task learning. In International Conference on Machine Learning (ICML), 2012.
20. Passos, A., Rai, P., Wainer, J., and Daume III, H. Flexible modeling of latent task structures in multitask learning. In International Conference on Machine Learning (ICML), 2012.
21. Rai, P. and Daumé III, H. Infinite predictor subspace models for multitask learning. Journal of Machine Learning Research - Proceedings Track, 9:613-620, 2010.
22. Rosenstein, M.T., Marx, Z., Kaelbling, L.P., and Dietterich, T.G. To transfer or not to transfer. In NIPS Workshop on Inductive Transfer, volume 10, 2005.
23. Sethuraman, J. A constructive definition of Dirichlet priors. Statistics Sinica, 4(2):639-650, 1994.
24. Teh, Y.W., Jordan, M.I., Beal, M.J., and Blei, D.M. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566-1581, 2006.
25. Teh, Y.W., Görür, D., and Ghahramani, Z. Stick-breaking construction for the Indian buffet process. Journal of Machine Learning Research - Proceedings Track, 2:556-563, 2007.
26. Thibaux, R. and Jordan, M.I. Hierarchical beta processes and the Indian buffet process. Journal of Machine Learning Research - Proceedings Track, 2:564-571, 2007.
27. Wang, X., Zhang, C., and Zhang, Z. Boosted multi-task learning for face verification with applications to web image and video search. In CVPR 2009. IEEE Conference on, pp. 142-149. IEEE, 2009.
28. Xue, Y., Liao, X., Carin, L., and Krishnapuram, B. Multi-task learning for classification with Dirichlet process priors. The Journal of Machine Learning Research, 8:35-63, 2007. (Pubitemid 46155123)