On multiplicative multitask feature learning

Advances in Neural Information Processing Systems

Volumn 3, Issue January, 2014, Pages 2411-2419

  Wang, Xin ^a   Bi, Jinbo ^a   Yu, Shipeng ^a   Sun, Jiangwen ^b  

^a University of Connecticut   (United States)

^b Health Services Innovation Center Siemens Healthcare   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE;

ANALYTICAL FORMULAS; EMPIRICAL STUDIES; FEATURE LEARNING; LEARNING FORMULATION; MODEL PARAMETERS; SHRINKAGE EFFECTS; SPECIFIC COMPONENT; STATE OF THE ART;

LEARNING ALGORITHMS;

EID: 84937832542     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. In Proceedings of NIPS 07, pages 41-48. 2007.
2. J. Bi, T. Xiong, S. Yu, M. Dundar, and R. B. Rao. An improved multi-task learning approach with applications in medical diagnosis. In Proceedings of ECML'08, pages 117-132, 2008.
3. J. Chen, J. Zhou, and J. Ye. Integrating low-rank and group-sparse structures for robust multitask learning. In Proceedings of KDD'11, pages 42-50, 2011.
4. T. Evgeniou and M. Pontil. Regularized multi-task learning. In Proceedings of KDD'04, pages 109-117, 2004.
5. P. Gong, J. Ye, and C. Zhang. Multi-stage multi-task feature learning. In Proceedings of NIPS'12, pages 1997-2005, 2012.
6. P. Gong, J. Ye, and C. Zhang. Robust multi-task feature learning. In Proceedings of KDD'12, pages 895-903, 2012.
7. I. R. Goodman and S. Kotz. Multivariate θ-generalized normal distributions. Journal of Multivariate Analysis, 3(2):204-219, 1973.
8. L. Jacob, B. Francis, and J.-P. Vert. Clustered multi-task learning: a convex formulation. 2008.
9. A. Jalali, S. Sanghavi, C. Ruan, and P. K. Ravikumar. A dirty model for multi-task learning. In Proceedings of NIPS'10, pages 964-972, 2010.
10. Z. Kang, K. Grauman, and F. Sha. Learning with whom to share in multi-task feature learning. In Proceedings of ICML'11, pages 521-528, 2011.
11. A. Kumar and H. Daume III. Learning task grouping and overlap in multi-task learning. In Proceedings of ICML'12, 2012.
12. S. Lee, J. Zhu, and E. Xing. Adaptive multi-task lasso: with application to eQTL detection. In Proceedings of NIPS'10, pages 1306-1314. 2010.
13. J. Liu, S. Ji, and J. Ye. Multi-task feature learning via efficient ℓ1-norm minimization. In Proceedings of UAI'09, pages 339-348, 2009.
14. A. Lozano and G. Swirszcz. Multi-level lasso for sparse multi-task regression. In Proceedings of ICML'12, pages 361-368, 2012.
15. G. Obozinski and B. Taskar. Multi-task feature selection. In Technical report, Statistics Department, UC Berkeley, 2006.
16. A. Passos, P. Rai, J. Wainer, and H. Daume III. Flexible modeling of latent task structures in multitask learning. In Proceedings of ICML'12, pages 1103-1110, 2012.
17. A. Quattoni, X. Carreras, M. Collins, and T. Darrell. An efficient projection for l1, infinity regularization. In Proceedings of ICML'09, pages 108-115, 2009.
18. P. Rai and H. Daume. Infinite predictor subspace models for multitask learning. In International Conference on Artificial Intelligence and Statistics, pages 613-620, 2010.
19. P. Tseng. Convergence of a block coordinate descent method for nondifferentiable minimization. Journal Optimization Theory Applications, 109(3):475-494, 2001.
20. B. A. Turlach, W. N. Wenables, and S. J. Wright. Simultaneous variable selection. Technometrics, 47(3):349-363, 2005.
21. T. Xiong, J. Bi, B. Rao, and V. Cherkassky. Probabilistic joint feature selection for multi-task learning. In Proceedings of SIAM International Conference on Data Mining (SDM), pages 69-76, 2006.
22. Y Zhang, D.-Y Yeung, and Q. Xu. Probabilistic multi-task feature selection. In Proceedings of NIPS'10, pages 2559-2567, 2010.
23. J. Zhou, J. Chen, and J. Ye. Clustered multi-task learning via alternating structure optimization. In Proceedings of NIPS'11, pages 702-710, 2011.
24. Y Zhou, R. Jin, and S. C. Hoi. Exclusive lasso for multi-task feature selection. In Proceedings of UAI'10, pages 988-995, 2010.