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Calibrated multi-task learning
Nie, Feiping1; Hu, Zhanxuan1; Li, Xuelong2
Conference Name24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Source PublicationKDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Conference Date2018-08-19
Conference PlaceLondon, United kingdom
PublisherAssociation for Computing Machinery
Contribution Rank2
AbstractThis paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), for learning multiple related regression tasks jointly. Instead of utilizing the nuclear norm, NC-CMTL adopts a non-convex low rank regularizer to explore the shared information among different tasks. In addition, considering that the regularization parameter for each regression task desponds on its noise level, we replace the least squares loss function by square-root loss function. Computationally, as proposed model has a non-smooth loss function and a non-convex regularization term, we construct an efficient re-weighted method to optimize it. Theoretically, we first present the convergence analysis of constructed method, and then prove that the derived solution is a stationary point of original problem. Particularly, the regularizer and optimization method used in this paper are also suitable for other rank minimization problems. Numerical experiments on both synthetic and real data illustrate the advantages of NC-CMTL over several state-of-the-art methods. © 2018 Association for Computing Machinery.
Indexed ByEI
EI Accession Number20183405705514
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Affiliation1.School of Computer Science, Center for OPTIMAL, Northwestern Polytechnical University, Xi'an, China;
2.OPTIMAL, Xian Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
Recommended Citation
GB/T 7714
Nie, Feiping,Hu, Zhanxuan,Li, Xuelong. Calibrated multi-task learning[C]:Association for Computing Machinery,2018:2012-2021.
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