메뉴 건너뛰기




Volumn , Issue , 2014, Pages 761-770

Efficient multi-task feature learning with calibration

Author keywords

accelerated gradient descent; calibration; dual problem; feature selection; multi task learning

Indexed keywords


EID: 84907020149     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623641     Document Type: Conference Paper
Times cited : (51)

References (38)
  • 1
    • 55149088329 scopus 로고    scopus 로고
    • 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
  • 4
    • 85014561619 scopus 로고    scopus 로고
    • A fast iterative shrinkage-thresholding algorithm for linear inverse problems
    • SIAM
    • A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009.
    • (2009) Journal on Imaging Sciences , vol.2 , Issue.1 , pp. 183-202
    • Beck, A.1    Teboulle, M.2
  • 5
    • 84887283313 scopus 로고    scopus 로고
    • A fast dual proximal gradient algorithm for convex minimization and applications
    • A. Beck and M. Teboulle. A fast dual proximal gradient algorithm for convex minimization and applications. Operations Research Letters, 42(1):1-6, 2014.
    • (2014) Operations Research Letters , vol.42 , Issue.1 , pp. 1-6
    • Beck, A.1    Teboulle, M.2
  • 6
    • 82255196005 scopus 로고    scopus 로고
    • Square-root lasso: Pivotal recovery of sparse signals via conic programming
    • A. Belloni, V. Chernozhukov, and L. Wang. Square-root lasso: pivotal recovery of sparse signals via conic programming. Biometrika, 98(4):791-806, 2011.
    • (2011) Biometrika , vol.98 , Issue.4 , pp. 791-806
    • Belloni, A.1    Chernozhukov, V.2    Wang, L.3
  • 8
    • 80051762104 scopus 로고    scopus 로고
    • Distributed optimization and statistical learning via the alternating direction method of multipliers
    • S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1):1-122, 2011.
    • (2011) Foundations and Trends® in Machine Learning , vol.3 , Issue.1 , pp. 1-122
    • Boyd, S.1    Parikh, N.2    Chu, E.3    Peleato, B.4    Eckstein, J.5
  • 12
    • 80052946258 scopus 로고    scopus 로고
    • Efficient euclidean projections via piecewise root finding and its application in gradient projection
    • P. Gong, K. Gai, and C. Zhang. Efficient euclidean projections via piecewise root finding and its application in gradient projection. Neurocomputing, 74(17):2754-2766, 2011.
    • (2011) Neurocomputing , vol.74 , Issue.17 , pp. 2754-2766
    • Gong, P.1    Gai, K.2    Zhang, C.3
  • 13
    • 84877724400 scopus 로고    scopus 로고
    • Multi-stage multi-task feature learning
    • P. Gong, J. Ye, and C. Zhang. Multi-stage multi-task feature learning. In NIPS, pages 1997-2005, 2012.
    • (2012) NIPS , pp. 1997-2005
    • Gong, P.1    Ye, J.2    Zhang, C.3
  • 14
    • 84866007553 scopus 로고    scopus 로고
    • 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
  • 17
    • 50549093271 scopus 로고    scopus 로고
    • Automated annotation of drosophila gene expression patterns using a controlled vocabulary
    • S. Ji, L. Sun, R. Jin, S. Kumar, and J. Ye. Automated annotation of drosophila gene expression patterns using a controlled vocabulary. Bioinformatics, 24(17):1881-1888, 2008.
    • (2008) Bioinformatics , vol.24 , Issue.17 , pp. 1881-1888
    • Ji, S.1    Sun, L.2    Jin, R.3    Kumar, S.4    Ye, J.5
  • 18
    • 77956548668 scopus 로고    scopus 로고
    • 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, 2009.
    • ICML, 2009
    • Kim, S.1    Xing, E.2
  • 21
    • 71149111015 scopus 로고    scopus 로고
    • Blockwise coordinate descent procedures for the multi-task lasso with applications to neural semantic basis discovery
    • H. Liu, M. Palatucci, and J. Zhang. Blockwise coordinate descent procedures for the multi-task lasso with applications to neural semantic basis discovery. In ICML, pages 649-656, 2009.
    • (2009) ICML , pp. 649-656
    • Liu, H.1    Palatucci, M.2    Zhang, J.3
  • 26
    • 84855412474 scopus 로고    scopus 로고
    • Oracle inequalities and optimal inference under group sparsity
    • K. Lounici, M. Pontil, S. Van De Geer, and A. Tsybakov. Oracle inequalities and optimal inference under group sparsity. The Annals of Statistics , 39(4):2164-2204, 2011.
    • (2011) The Annals of Statistics , vol.39 , Issue.4 , pp. 2164-2204
    • Lounici, K.1    Pontil, M.2    Van De Geer, S.3    Tsybakov, A.4
  • 27
    • 17444406259 scopus 로고    scopus 로고
    • Smooth minimization of non-smooth functions
    • Y. Nesterov. Smooth minimization of non-smooth functions. Mathematical Programming , 103(1):127-152, 2005.
    • (2005) Mathematical Programming , vol.103 , Issue.1 , pp. 127-152
    • Nesterov, Y.1
  • 33
    • 84863338429 scopus 로고    scopus 로고
    • Heterogeneous multitask learning with joint sparsity constraints
    • X. Yang, S. Kim, and E. Xing. Heterogeneous multitask learning with joint sparsity constraints. In NIPS, 2009.
    • NIPS, 2009
    • Yang, X.1    Kim, S.2    Xing, E.3
  • 34
    • 85162027638 scopus 로고    scopus 로고
    • Probabilistic multi-task feature selection
    • Y. Zhang, D. Yeung, and Q. Xu. Probabilistic multi-task feature selection. In NIPS, 2010.
    • NIPS, 2010
    • Zhang, Y.1    Yeung, D.2    Xu, Q.3
  • 36
    • 84866037290 scopus 로고    scopus 로고
    • Modeling disease progression via fused sparse group lasso
    • J. Zhou, J. Liu, V. Narayan, and J. Ye. Modeling disease progression via fused sparse group lasso. In SIGKDD, pages 1095-1103, 2012.
    • (2012) SIGKDD , pp. 1095-1103
    • Zhou, J.1    Liu, J.2    Narayan, V.3    Ye, J.4
  • 37
    • 80052666240 scopus 로고    scopus 로고
    • A multi-task learning formulation for predicting disease progression
    • J. Zhou, L. Yuan, J. Liu, and J. Ye. A multi-task learning formulation for predicting disease progression. In SIGKDD, pages 814-822, 2011.
    • (2011) SIGKDD , pp. 814-822
    • Zhou, J.1    Yuan, L.2    Liu, J.3    Ye, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.