-
1
-
-
55149088329
-
Convex multi-task feature learning
-
A. Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
-
(2008)
Machine Learning
, vol.73
, Issue.3
, pp. 243-272
-
-
Argyriou, A.1
Evgeniou, T.2
Pontil, M.3
-
2
-
-
56049124852
-
An improved multi-task learning approach with applications in medical diagnosis
-
J. Bi, T. Xiong, S. Yu, M. Dundar, and R. Rao. An improved multi-task learning approach with applications in medical diagnosis. Machine Learning and Knowledge Discovery in Databases, pages 117-132, 2008.
-
(2008)
Machine Learning and Knowledge Discovery in Databases
, pp. 117-132
-
-
Bi, J.1
Xiong, T.2
Yu, S.3
Dundar, M.4
Rao, R.5
-
4
-
-
77956208061
-
Learning incoherent sparse and low-rank patterns from multiple tasks
-
J. Chen, J. Liu, and J. Ye. Learning incoherent sparse and low-rank patterns from multiple tasks. In SIGKDD, pages 1179-1188, 2010.
-
(2010)
SIGKDD
, pp. 1179-1188
-
-
Chen, J.1
Liu, J.2
Ye, J.3
-
5
-
-
33144483155
-
Stable recovery of sparse overcomplete representations in the presence of noise
-
D. Donoho, M. Elad, and V. Temlyakov. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Information Theory, 52(1):6-18, 2006.
-
(2006)
IEEE Transactions on Information Theory
, vol.52
, Issue.1
, pp. 6-18
-
-
Donoho, D.1
Elad, M.2
Temlyakov, V.3
-
6
-
-
12244250351
-
Regularized multi-task learning
-
T. Evgeniou and M. Pontil. Regularized multi-task learning. In SIGKDD, pages 109-117, 2004.
-
(2004)
SIGKDD
, pp. 109-117
-
-
Evgeniou, T.1
Pontil, M.2
-
8
-
-
84866007553
-
Robust multi-task feature learning
-
P. Gong, J. Ye, and C. Zhang. Robust multi-task feature learning. In SIGKDD, pages 895-903, 2012.
-
(2012)
SIGKDD
, pp. 895-903
-
-
Gong, P.1
Ye, J.2
Zhang, C.3
-
9
-
-
85162062975
-
A dirty model for multi-task learning
-
A. Jalali, P. Ravikumar, S. Sanghavi, and C. Ruan. A dirty model for multi-task learning. In NIPS, pages 964-972, 2010.
-
(2010)
NIPS
, pp. 964-972
-
-
Jalali, A.1
Ravikumar, P.2
Sanghavi, S.3
Ruan, C.4
-
10
-
-
77956548668
-
Tree-guided group lasso for multi-task regression with structured sparsity
-
S. Kim and E. Xing. Tree-guided group lasso for multi-task regression with structured sparsity. In ICML, pages 543-550, 2009.
-
(2009)
ICML
, pp. 543-550
-
-
Kim, S.1
Xing, E.2
-
11
-
-
84898059927
-
Taking advantage of sparsity in multi-task learning
-
K. Lounici, M. Pontil, A. Tsybakov, and S. Van De Geer. Taking advantage of sparsity in multi-task learning. In COLT, pages 73-82, 2009.
-
(2009)
COLT
, pp. 73-82
-
-
Lounici, K.1
Pontil, M.2
Tsybakov, A.3
Geer De S.Van4
-
13
-
-
79952934740
-
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
-
S. Negahban and M.Wainwright. Estimation of (near) low-rank matrices with noise and high-dimensional scaling. The Annals of Statistics, 39(2):1069-1097, 2011.
-
(2011)
The Annals of Statistics
, vol.39
, Issue.2
, pp. 1069-1097
-
-
Negahban, S.1
Wainwright, M.2
-
14
-
-
34948865158
-
Multi-task feature selection
-
G. Obozinski, B. Taskar, and M. Jordan. Multi-task feature selection. Statistics Department, UC Berkeley, Tech. Rep, 2006.
-
(2006)
Statistics Department, UC Berkeley, Tech. Rep
-
-
Obozinski, G.1
Taskar, B.2
Jordan, M.3
-
15
-
-
79551607002
-
Support union recovery in high-dimensional multivariate regression
-
G. Obozinski, M. Wainwright, and M. Jordan. Support union recovery in high-dimensional multivariate regression. Annals of statistics, 39(1):1-47, 2011.
-
(2011)
Annals of Statistics
, vol.39
, Issue.1
, pp. 1-47
-
-
Obozinski, G.1
Wainwright, M.2
Jordan, M.3
-
16
-
-
85162530133
-
Large margin multi-task metric learning
-
S. Parameswaran and K. Weinberger. Large margin multi-task metric learning. In NIPS, pages 1867-1875, 2010.
-
(2010)
NIPS
, pp. 1867-1875
-
-
Parameswaran, S.1
Weinberger, K.2
-
17
-
-
85162067125
-
Multitask learning without label correspondences
-
N. Quadrianto, A. Smola, T. Caetano, S. Vishwanathan, and J. Petterson. Multitask learning without label correspondences. In NIPS, pages 1957-1965, 2010.
-
(2010)
NIPS
, pp. 1957-1965
-
-
Quadrianto, N.1
Smola, A.2
Caetano, T.3
Vishwanathan, S.4
Petterson, J.5
-
18
-
-
84899006514
-
Learning gaussian process kernels via hierarchical bayes
-
A. Schwaighofer, V. Tresp, and K. Yu. Learning gaussian process kernels via hierarchical bayes. In NIPS, pages 1209-1216, 2005.
-
(2005)
NIPS
, pp. 1209-1216
-
-
Schwaighofer, A.1
Tresp, V.2
Yu, K.3
-
19
-
-
77955054299
-
On the conditions used to prove oracle results for the lasso
-
S. Van De Geer and P. Bühlmann. On the conditions used to prove oracle results for the lasso. Electronic Journal of Statistics, 3:1360-1392, 2009.
-
(2009)
Electronic Journal of Statistics
, vol.3
, pp. 1360-1392
-
-
Geer De S.Van1
Bühlmann, P.2
-
20
-
-
84863338429
-
Heterogeneous multitask learning with joint sparsity constraints
-
X. Yang, S. Kim, and E. Xing. Heterogeneous multitask learning with joint sparsity constraints. In NIPS, pages 2151-2159, 2009.
-
(2009)
NIPS
, pp. 2151-2159
-
-
Yang, X.1
Kim, S.2
Xing, E.3
-
21
-
-
31844442664
-
Learning gaussian processes from multiple tasks
-
K. Yu, V. Tresp, and A. Schwaighofer. Learning gaussian processes from multiple tasks. In ICML, pages 1012-1019, 2005.
-
(2005)
ICML
, pp. 1012-1019
-
-
Yu, K.1
Tresp, V.2
Schwaighofer, A.3
-
22
-
-
50949096321
-
The sparsity and bias of the lasso selection in high-dimensional linear regression
-
C. Zhang and J. Huang. The sparsity and bias of the lasso selection in high-dimensional linear regression. The Annals of Statistics, 36(4):1567-1594, 2008.
-
(2008)
The Annals of Statistics
, vol.36
, Issue.4
, pp. 1567-1594
-
-
Zhang, C.1
Huang, J.2
-
23
-
-
84871532743
-
A general theory of concave regularization for high dimensional sparse estimation problems
-
C. Zhang and T. Zhang. A general theory of concave regularization for high dimensional sparse estimation problems. Statistical Science, 2012.
-
(2012)
Statistical Science
-
-
Zhang, C.1
Zhang, T.2
-
24
-
-
79951845184
-
Learning multiple related tasks using latent independent component analysis
-
J. Zhang, Z. Ghahramani, and Y. Yang. Learning multiple related tasks using latent independent component analysis. In NIPS, pages 1585-1592, 2006.
-
(2006)
NIPS
, pp. 1585-1592
-
-
Zhang, J.1
Ghahramani, Z.2
Yang, Y.3
-
26
-
-
77951191949
-
Analysis of multi-stage convex relaxation for sparse regularization
-
T. Zhang. Analysis of multi-stage convex relaxation for sparse regularization. JMLR, 11:1081-1107, 2010.
-
(2010)
JMLR
, vol.11
, pp. 1081-1107
-
-
Zhang, T.1
-
27
-
-
84877773187
-
Multi-stage convex relaxation for feature selection
-
T. Zhang. Multi-stage convex relaxation for feature selection. Bernoulli, 2012.
-
(2012)
Bernoulli
-
-
Zhang, T.1
-
28
-
-
80052689284
-
Multi-task learning using generalized t process
-
Y. Zhang and D. Yeung. Multi-task learning using generalized t process. In AISTATS, 2010.
-
(2010)
AISTATS
-
-
Zhang, Y.1
Yeung, D.2
-
29
-
-
85162400077
-
Clustered multi-task learning via alternating structure optimization
-
J. Zhou, J. Chen, and J. Ye. Clustered multi-task learning via alternating structure optimization. In NIPS, pages 702-710, 2011.
-
(2011)
NIPS
, pp. 702-710
-
-
Zhou, J.1
Chen, J.2
Ye, J.3
|