-
1
-
-
33745561205
-
An introduction to variable and feature selection
-
I. Guyon and A. Elisseeff, "An introduction to variable and feature selection, " Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
-
(2003)
Journal of Machine Learning Research
, vol.3
, pp. 1157-1182
-
-
Guyon, I.1
Elisseeff, A.2
-
3
-
-
79952797027
-
Sparse multivariate regression with covariance estimation
-
A. J. Rothman, E. Levina, and J. Zhu, " Sparse multivariate regression with covariance estimation," Journal of Computational and Graphical Statistics, vol. 19, no. 4, pp. 947-962, 2010.
-
(2010)
Journal of Computational and Graphical Statistics
, vol.19
, Issue.4
, pp. 947-962
-
-
Rothman, A.J.1
Levina, E.2
Zhu, J.3
-
4
-
-
66349089385
-
A multivariate regression approach to association analysis of a quantitative trait network
-
S. Kim, K.-A. Sohn, and E. P. Xing, " A multivariate regression approach to association analysis of a quantitative trait network," Bioinformatics, vol. 25, no. 12, pp. 204-212, 2009.
-
(2009)
Bioinformatics
, vol.25
, Issue.12
, pp. 204-212
-
-
Kim, S.1
Sohn, K.-A.2
Xing, E.P.3
-
5
-
-
0000927638
-
Predicting m ultivariate responses in multiple linear regression
-
L. Breiman and J. H. Friedman, "Predicting m ultivariate responses in multiple linear regression," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 59, no. 1, pp. 3-54, 1997.
-
(1997)
Journal of the Royal Statistical Society: Series B (Statistical Methodology
, vol.59
, Issue.1
, pp. 3-54
-
-
Breiman, L.1
Friedman, J.H.2
-
6
-
-
70450192619
-
Structured output-Associative regression
-
L. Bo and C. Sminchisescu, "Structured output-Associative regression, " in CVPR, 2009.
-
(2009)
CVPR
-
-
Bo, L.1
Sminchisescu, C.2
-
7
-
-
79958706548
-
Output-Associative rvm regression for dimensional and continuous emotion prediction
-
M. A. Nicolaou, H. Gunes, and M. Pantic, " Output-Associative rvm regression for dimensional and continuous emotion prediction," in FG, 2011.
-
(2011)
FG
-
-
Nicolaou, M.A.1
Gunes, H.2
Pantic, M.3
-
8
-
-
84867137594
-
The landmark selection method for multiple output prediction
-
K. Balasubramanian and G. Lebanon, "The landmark selection method for multiple output prediction, " in ICML, 2012.
-
(2012)
ICML
-
-
Balasubramanian, K.1
Lebanon, G.2
-
9
-
-
34447335946
-
-
Department of Statistics, UC Berkeley, Tech. Rep
-
P. Zhao, G. Rocha, and B. Yu, "Grouped and hierar chical model selection through composite absolute penalties," Department of Statistics, UC Berkeley, Tech. Rep, vol. 703, 2006.
-
(2006)
Grouped and Hierar Chical Model Selection Through Composite Absolute Penalties
, vol.703
-
-
Zhao, P.1
Rocha, G.2
Yu, B.3
-
10
-
-
80053450397
-
-
arXiv preprint arXiv: 1005.3579
-
X. Chen, S. Kim, Q. Lin, J. G. Carbonell, and E. P. Xing, " Graphstructured multi-task regression and an efficient optimization method for general fused lasso," arXiv preprint arXiv:1005.3579, 2010.
-
(2010)
Graphstructured Multi-Task Regression and An Efficient Optimization Method for General Fused Lasso
-
-
Chen, X.1
Kim, S.2
Lin, Q.3
Carbonell, J.G.4
Xing, E.P.5
-
11
-
-
84870038047
-
Tree-guided group lasso for multi-respo nse regression with structured sparsity, with an application to eqtl mapping
-
S. Kim and E. P. Xing, "Tree-guided group lasso for multi-respo nse regression with structured sparsity, with an application to eqtl mapping," The Annals of Applied Statistics, vol. 6, no. 3, pp. 1095-1117, 2012.
-
(2012)
The Annals of Applied Statistics
, vol.6
, Issue.3
, pp. 1095-1117
-
-
Kim, S.1
Xing, E.P.2
-
12
-
-
1242263806
-
The generalized lasso
-
V. Roth, "The generalized lasso, " IEEE Tra nsactions on Neural Networks, vol. 15, no. 1, pp. 16-28, 2004.
-
(2004)
IEEE Tra Nsactions on Neural Networks
, vol.15
, Issue.1
, pp. 16-28
-
-
Roth, V.1
-
13
-
-
70350092487
-
Sparse additive models
-
P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman, " Sparse additive models," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 71, no. 5, pp. 1009-1030, 2009.
-
(2009)
Journal of the Royal Statistical Society: Series B (Statistical Methodology
, vol.71
, Issue.5
, pp. 1009-1030
-
-
Ravikumar, P.1
Lafferty, J.2
Liu, H.3
Wasserman, L.4
-
14
-
-
85161965875
-
Predicting execution time of computer programs using sparse polynomial regression
-
L. Huang, J. Jia, B. Yu, B.-G. Chun, P. Maniatis, and M. Naik, " Predicting execution time of computer programs using sparse polynomial regression," in NIPS, 2010.
-
(2010)
NIPS
-
-
Huang, L.1
Jia, J.2
Yu, B.3
Chun, B.-G.4
Maniatis, P.5
Naik, M.6
-
15
-
-
84892986309
-
High-dimensional feature selection by feature-wise kernelized lasso
-
M. Yamada,W. Jitkrittum,L. Sigal,E. P. Xing,M. Sugiyama High-dimensional feature selection by feature-wise kernelized lasso Neural Computation 26, 1, 185-207, 2014.
-
(2014)
Neural Computation
, vol.26
, Issue.1
, pp. 185-207
-
-
Yamada, M.1
Jitkrittum, W.2
Sigal, L.3
Xing, E.P.4
Sugiyama, M.5
-
16
-
-
84879398938
-
A lasso for hierarchical interactions
-
J. Bien, J. Taylor, and R. Tibshirani, "A lasso for hierarchical interactions, " The Annals of S tatistics, vol. 41, no. 3, pp. 1111-1141, 2013.
-
(2013)
The Annals of S Tatistics
, vol.41
, Issue.3
, pp. 1111-1141
-
-
Bien, J.1
Taylor, J.2
Tibshirani, R.3
-
18
-
-
0345327592
-
A simple and efficient algorithm for gene selection using sparse logistic regression
-
S. K. Shevade and S. S. Keerthi, " A simple and efficient algorithm for gene selection using sparse logistic regression," Bioinformatics, vol. 19, no. 17, pp. 2246-2253, 2003.
-
(2003)
Bioinformatics
, vol.19
, Issue.17
, pp. 2246-2253
-
-
Shevade, S.K.1
Keerthi, S.S.2
-
20
-
-
84865105673
-
Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix
-
Z. Lin, A. Ganesh, J. Wright, L. Wu, M. Chen, and Y. Ma, " Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix," Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), vol. 61, 2009.
-
(2009)
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP
, vol.61
-
-
Lin, Z.1
Ganesh, A.2
Wright, J.3
Wu, L.4
Chen, M.5
Ma, Y.6
-
21
-
-
77951291046
-
A singular value thresholding algorithm for matrix completion
-
J.-F. Cai, E. J. Candés, and Z. Shen, " A singular value thresholding algorithm for matrix completion," SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956-1982, 2010.
-
(2010)
SIAM Journal on Optimization
, vol.20
, Issue.4
, pp. 1956-1982
-
-
Cai, J.-F.1
Candés, E.J.2
Shen, Z.3
-
22
-
-
34948865158
-
-
Statistics Department, UC Berkeley, Tech. Rep
-
G. O bozinski, B. Taskar, and M. I. Jordan, "Multi-task feature selection, " Statistics Department, UC Berkeley, Tech. Rep, 2006.
-
(2006)
Multi-task Feature Selection
-
-
Bozinski, G.O.1
Taskar, B.2
Jordan, M.I.3
-
23
-
-
0031345518
-
Algorithm 778: L-bfgs b: Fortran subroutines for large-scale bound-constrained optimization
-
C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, "Algorithm 778: L-bfgs b: Fortran subroutines for large-scale bound-constrained optimization," ACM Transactions on Mathematical Software (TOMS), vol. 23, no. 4, pp. 550-560, 1997.
-
(1997)
ACM Transactions on Mathematical Software (TOMS
, vol.23
, Issue.4
, pp. 550-560
-
-
Zhu, C.1
Byrd, R.H.2
Lu, P.3
Nocedal, J.4
-
24
-
-
33749256006
-
Maximum margin semisupervised learning for structured variables
-
Y. Altun, M. Belkin, and D. A. Mcallester, " Maximum margin semisupervised learning for structured variables," in NIPS, 2005.
-
(2005)
NIPS
-
-
Altun, Y.1
Belkin, M.2
McAllester, D.A.3
-
25
-
-
85047013878
-
Structured learning with approximate inference
-
A. Kulesza and F. Pereira, "Structured learning with approximate inference, " in NIPS, 2007.
-
(2007)
NIPS
-
-
Kulesza, A.1
Pereira, F.2
-
26
-
-
84893391288
-
Hc-search: Learning heuristics and cost functions for structured prediction
-
J. R. Doppa, A. Fern, and P. Tadepalli, "Hc-search: Learning heuristics and cost functions for structured prediction, " in AAAI, 2013.
-
(2013)
AAAI
-
-
Doppa, J.R.1
Fern, A.2
Tadepalli, P.3
-
27
-
-
70350619001
-
Learning t o localize objects with structured output regression
-
M. B. Blaschko and C. H. Lampert, "Learning t o localize objects with structured output regression," in ECCV, 2008.
-
(2008)
ECCV
-
-
Blaschko, M.B.1
Lampert, C.H.2
-
28
-
-
71149086466
-
Learning structural svms with latent variables
-
C.-N. J. Yu and T. Joachims, "Learning structural svms with latent variables, " in ICML, 2009.
-
(2009)
ICML
-
-
Yu, C.-N.J.1
Joachims, T.2
-
30
-
-
79952307213
-
Alternating direction algorithms for-1-problems in compressive sensing
-
J. Yang and Y. Zhang, "Alternating direction algorithms for-1-problems in compressive sensing, " SIAM journal on scientific computing, vol. 33, no. 1, pp. 250-278, 2011.
-
(2011)
SIAM Journal on Scientific Computing
, vol.33
, Issue.1
, pp. 250-278
-
-
Yang, J.1
Zhang, Y.2
-
31
-
-
70049102918
-
Adaptive forward-backward greedy algorithm for sparse learning with linear models
-
T. Zhang, " Adaptive forward-backward greedy algorithm for sparse learning with linear models," in NIPS, 2008.
-
(2008)
NIPS
-
-
Zhang, T.1
-
32
-
-
0028428774
-
A database for handwritten text recognition research
-
J. J. Hull, "A database for handwritten text recognition research, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 550-554, 1994.
-
(1994)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.16
, Issue.5
, pp. 550-554
-
-
Hull, J.J.1
-
33
-
-
85156188079
-
Kernel dependency estimation
-
J. Weston, O. Chapelle, A. Elisseeff, B. Schölkopf, and V. Vapnik, " Kernel dependency estimation," in NIPS, 2002.
-
(2002)
NIPS
-
-
Weston, J.1
Chapelle, O.2
Elisseeff, A.3
Schölkopf, B.4
Vapnik, V.5
-
34
-
-
33846114377
-
The adaptive lasso and its oracle properties
-
H. Zou, "The adaptive lasso and its oracle properties, " Journal of the American statistical association, vol. 101, no. 476, pp. 1418-1429, 2006.
-
(2006)
Journal of the American Statistical Association
, vol.101
, Issue.476
, pp. 1418-1429
-
-
Zou, H.1
-
35
-
-
84867119971
-
Maximum margin output coding
-
Y. Zhang and J. Schneider, "Maximum margin output coding, " in ICML, 2012.
-
(2012)
ICML
-
-
Zhang, Y.1
Schneider, J.2
-
36
-
-
84862283912
-
Multi-label output codes using canonical correlation analysis
-
Y. Zhang,J. Schneider Multi-label output codes using canonical correlation analysis Journal of Machine Learning Research 15, 873-882, 2011.
-
(2011)
Journal of Machine Learning Research
, vol.15
, pp. 873-882
-
-
Zhang, Y.1
Schneider, J.2
-
37
-
-
77956528679
-
Multi-label prediction via compressed sensing
-
D. Hsu, S. M. Kakade, J. Langford, and T. Zhang, " Multi-label prediction via compressed sensing," in NIPS, 2009.
-
(2009)
NIPS
-
-
Hsu, D.1
Kakade, S.M.2
Langford, J.3
Zhang, T.4
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