-
1
-
-
34547979771
-
Uncovering shared structures in multi-class classification
-
Amit, Yonatan, Fink, Michael, Srebro, Nathan, and Ullman, Shimon. Uncovering shared structures in multi-class classification. In Proceedings of the 24th International Conference on Machine learning, pp. 17-24, 2007.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning
, pp. 17-24
-
-
Amit, Y.1
Fink, M.2
Srebro, N.3
Ullman, S.4
-
2
-
-
27844439373
-
A framework for learning predictive structures from multiple tasks and unlabeled data
-
Ando, Rie Kubota and Zhang, Tong. A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res., 6:1817-1853, 2005.
-
(2005)
J. Mach. Learn. Res.
, vol.6
, pp. 1817-1853
-
-
Ando, R.K.1
Zhang, T.2
-
3
-
-
55149088329
-
Convex multi-task feature learning
-
Argyriou, Andreas, Evgeniou, Theodoros, and Pontil, Massimiliano. Convex multi-task feature learning. Machine Learning, 73:243-272, 2008a.
-
(2008)
Machine Learning
, vol.73
, pp. 243-272
-
-
Argyriou, A.1
Evgeniou, T.2
Pontil, M.3
-
4
-
-
56049122238
-
An algorithm for transfer learning in a heterogeneous environment
-
Argyriou, Andreas, Maurer, Andreas, and Pontil, Massimiliano. An algorithm for transfer learning in a heterogeneous environment. In Proc. of ECML/PKDD, pp. 71-85, 2008b.
-
(2008)
Proc. of ECML/PKDD
, pp. 71-85
-
-
Argyriou, A.1
Maurer, A.2
Pontil, M.3
-
5
-
-
85162007328
-
A spectral regularization framework for multi-task structure learning
-
Platt, J.C., Koller, D., Singer, Y., and Roweis, S. (eds.), MIT Press, Cambridge, MA
-
Argyriou, Andreas, Micchelli, Charles A., Pontil, Massimiliano, and Ying, Yiming. A spectral regularization framework for multi-task structure learning. In Platt, J.C., Koller, D., Singer, Y., and Roweis, S. (eds.), Advances in Neural Information Processing Systems 20, pp. 25-32. MIT Press, Cambridge, MA, 2008c.
-
(2008)
Advances in Neural Information Processing Systems
, vol.20
, pp. 25-32
-
-
Argyriou, A.1
Micchelli, C.A.2
Pontil, M.3
Ying, Y.4
-
6
-
-
0031189914
-
Multitask learning
-
Caruana, Rich. Multitask learning. MLJ, 28:41-75, 1997.
-
(1997)
MLJ
, vol.28
, pp. 41-75
-
-
Caruana, R.1
-
7
-
-
80053148229
-
Bayesian multitask learning with latent hierarchies
-
Arlington, Virginia, United States, AUAI Press
-
Daumé, III, Hal. Bayesian multitask learning with latent hierarchies. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 135-142, Arlington, Virginia, United States, 2009. AUAI Press.
-
(2009)
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
, pp. 135-142
-
-
Daumé III, H.1
-
8
-
-
12244250351
-
Regularized multi-task learning
-
Evgeniou, Theodoros and Pontil, Massimiliano. Regularized multi-task learning. In KDD, pp. 109-117, 2004.
-
(2004)
KDD
, pp. 109-117
-
-
Evgeniou, T.1
Pontil, M.2
-
10
-
-
80052908079
-
Sharing features between objects and their attributes
-
Hwang, Sung Ju, Sha, Fei, and Grauman, Kristen. Sharing features between objects and their attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO., 2011.
-
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO., 2011
-
-
Hwang, S.J.1
Sha, F.2
Grauman, K.3
-
11
-
-
84858783652
-
Clustered multi-task learning: A convex formulation
-
Jacob, L., Bach, F., and Vert, J.P. Clustered multi-task learning: A convex formulation. Advances in Neural Information Processing Systems, 21:745-752, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, vol.21
, pp. 745-752
-
-
Jacob, L.1
Bach, F.2
Vert, J.P.3
-
13
-
-
70450172710
-
Learning to detect unseen object classes by between-class attribute transfer
-
Lampert, C.H., Nickisch, H., and Harmeling, S. Learning to detect unseen object classes by between-class attribute transfer. In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 951-958, 2009.
-
(2009)
Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, pp. 951-958
-
-
Lampert, C.H.1
Nickisch, H.2
Harmeling, S.3
-
14
-
-
0032203257
-
Gradient-based learning applied to document recognitio
-
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognitio. Proc. of IEEE, 86:2278-2324, 1998.
-
(1998)
Proc. of IEEE
, vol.86
, pp. 2278-2324
-
-
LeCun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
15
-
-
34547989609
-
Learning a meta-level prior for feature relevance from multiple related tasks
-
ACM
-
Lee, S.I., Chatalbashev, V., Vickrey, D., and Koller, D. Learning a meta-level prior for feature relevance from multiple related tasks. In Proceedings of the 24th International Conference on Machine learning, pp. 496. ACM, 2007.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning
, pp. 496
-
-
Lee, S.I.1
Chatalbashev, V.2
Vickrey, D.3
Koller, D.4
-
16
-
-
80053145416
-
Multi-task feature learning via efficient 1 2, 1-norm minimization
-
AUAI Press
-
Liu, J., Ji, S., and Ye, J. Multi-task feature learning via efficient 1 2, 1-norm minimization. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 339-348. AUAI Press, 2009.
-
(2009)
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
, pp. 339-348
-
-
Liu, J.1
Ji, S.2
Ye, J.3
-
18
-
-
37849035696
-
The group LASSO for logistic regression
-
Meier, Lukas, van de Geer, Sara, and Bühlmann, Peter. The group LASSO for logistic regression. Journal Of The Royal Statistical Society Series B, 70(1):53-71, 2008.
-
(2008)
Journal of the Royal Statistical Society Series B
, vol.70
, Issue.1
, pp. 53-71
-
-
Meier, L.1
Van De Geer, S.2
Bühlmann, P.3
-
19
-
-
70049105714
-
Joint covariate selection and joint subspace selection for multiple classification problems
-
Obozinski, G., Taskar, B., and Jordan, M.I. Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing, pp. 1-22, 2009.
-
(2009)
Statistics and Computing
, pp. 1-22
-
-
Obozinski, G.1
Taskar, B.2
Jordan, M.I.3
-
20
-
-
85162530133
-
Large margin multi-task metric learning
-
Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R.S., and Culotta, A. (eds.), MIT Press
-
Parameswaran, Shibin and Weinberger, Kilian. Large margin multi-task metric learning. In Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R.S., and Culotta, A. (eds.), Advances in Neural Information Processing Systems 23, pp. 1867-1875. MIT Press, 2010.
-
(2010)
Advances in Neural Information Processing Systems
, vol.23
, pp. 1867-1875
-
-
Parameswaran, S.1
Weinberger, K.2
-
21
-
-
51949094374
-
Transfer learning for image classification with sparse prototype representations
-
Quattoni, Ariadna, Collins, Michael, and Darrell, Trevor. Transfer learning for image classification with sparse prototype representations. In Proc. of CVPR, 2008.
-
Proc. of CVPR, 2008
-
-
Quattoni, A.1
Collins, M.2
Darrell, T.3
-
22
-
-
0043217278
-
-
Kluwer Academic Publishers, Norwell, MA, USA
-
Thrun, Sebastian and O'Sullivan, Joseph. Clustering learning tasks and the selective cross-task transfer of knowledge, pp. 235-257. Kluwer Academic Publishers, Norwell, MA, USA, 1998.
-
(1998)
Clustering Learning Tasks and the Selective Cross-task Transfer of Knowledge
, pp. 235-257
-
-
Thrun, S.1
O'Sullivan, J.2
-
23
-
-
0003901612
-
-
Kluwer Academic Publishers, Norwell, MA, USA, ISBN 0-7923-8047-9
-
Thrun, Sebastian and Pratt, Lorien. Learning to learn. Kluwer Academic Publishers, Norwell, MA, USA, 1998. ISBN 0-7923-8047-9.
-
(1998)
Learning to Learn
-
-
Thrun, S.1
Pratt, L.2
-
24
-
-
34047200109
-
Sharing visual features for multiclass and multiview object detection
-
Torralba, Antonio, Murphy, Kevin P., and Freeman, William T. Sharing visual features for multiclass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell., 29:854-869, 2007.
-
(2007)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.29
, pp. 854-869
-
-
Torralba, A.1
Murphy, K.P.2
Freeman, W.T.3
-
26
-
-
31844442664
-
Learning gaussian processes from multiple tasks
-
New York, NY, USA, ACM
-
Yu, Kai, Tresp, Volker, and Schwaighofer, Anton. Learning gaussian processes from multiple tasks. In Proceedings of the 22nd international conference on Machine learning, pp. 1012-1019, New York, NY, USA, 2005. ACM.
-
(2005)
Proceedings of the 22nd International Conference on Machine Learning
, pp. 1012-1019
-
-
Yu, K.1
Tresp, V.2
Schwaighofer, A.3
-
27
-
-
80053162594
-
A Convex Formulation for Learning Task Relationships in Multi-Task Learning
-
Zhang, Y. and Yeung, D.Y. A Convex Formulation for Learning Task Relationships in Multi-Task Learning. In UAI, 2010.
-
(2010)
UAI
-
-
Zhang, Y.1
Yeung, D.Y.2
|