1 |
J. Wang, M. Kolar, and N. Srerbo, "Distributed multi-task learning," in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Cadiz, Spain, 2016, pp. 751-760.
|
2 |
R. Tibshirani, "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Societ B, vol. 73, no. 3, pp. 273-282, 2011.
DOI
|
3 |
J. Wright, A. Ganesh, S. Rao, and Y. Ma, "Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization," Coordinated Science Laboratory, University of Illinois, Urbana, IL, Report No. UILU-ENG-09-2210(DC-243), 2009.
|
4 |
J. Konecny, H. Brendan McMahan, F. X. Yu, A. T. Suresh, D. Bacon, and P. Richtarik, "Federated learning: strategies for improving communication efficiency," 2017 [Online]. Available: https://arxiv.org/abs/1610.05492.
|
5 |
M. Zhao, H. Zhang, W. Cheng, and Z. Zhang, "Joint lp- and l2,p-norm minimization for subspace clustering with outlier pursuit," in Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 2016, pp. 3658-3665.
|
6 |
H. Brendan McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, "Communication-efficient learning of deep networks from decentralized data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, 2017, pp. 1273-1282.
|
7 |
Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated machine learning: concept and applications," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 2, article no. 12, 2019. https://doi.org/10.1145/3298981
DOI
|
8 |
K. S. Kim and S. Y. Chung, "Greedy subspace pursuit for joint sparse recovery," Journal of Computational and Applied Mathematics, vol. 352, pp. 308-327, 2019.
DOI
|
9 |
X. Ding, Y. Chen, Z. Tang, and Y. Huang, "Camera identification based on domain knowledge-driven deep multi-task learning," IEEE Access, vol. 7, pp. 25878-25890, 2019.
DOI
|
10 |
D. Mateos-Nunez, J. Cortes, and J. Cortes, "Distributed optimization for multi-task learning via nuclear-norm approximation," IFAC-PapersOnLine, vol. 48, no. 22, pp. 64-69, 2015.
|
11 |
M. Zhang, Y. Yang, H. Zhang, F. Shen, and D. Zhang, "L2,p-norm and sample constraint based feature selection and classification for AD diagnosis," Neurocomputing, vol. 195, pp. 104-111, 2016.
DOI
|
12 |
R. Caruana, "Multitask learning," Machine Learning, vol. 28, no. 1, pp. 41-75, 1997.
DOI
|
13 |
D. Zhang and D. Shen, "Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease," NeuroImage, vol. 59, no. 2, pp. 895-907, 2012.
DOI
|
14 |
Z. Hu, B. Li, and J. Luo, "Time-and cost-efficient task scheduling across geo-distributed data centers," IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 3, pp. 705-718, 2018.
DOI
|
15 |
T. Evgeniou and M. Pontil, "Regularized multi-task learning," in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, 2004, pp. 109-117.
|
16 |
X. Cai, F. Nie, H. Huang, and C. Ding, "Multi-class l2,1-norm support vector machine," in Proceedings of 2011 IEEE 11th International Conference on Data Mining, Vancouver, Canada, 2011, pp. 91-100.
|
17 |
P. Heins, M. Moeller, and M. Burger, "Locally sparse reconstruction using the l1,∞-norm," Inverse Problems & Imaging, vol. 9, no. pp. 1093-1137, 2015.
DOI
|
18 |
P. E. Gill, W. Murray, and M. A. Saunders, "SNOPT: an SQP algorithm for large-scale constrained optimization," SIAM Review, vol. 47, no. 1, pp. 99-131, 2005.
DOI
|
19 |
S. Yi, Y. Liang, Z. He, Y. Li, and Y. M. Cheung, "Dual pursuit for subspace learning," IEEE Transactions on Multimedia, vol. 21, no. 6, pp. 1399-1411, 2019.
DOI
|
20 |
N. Tottenham, J. W. Tanaka, A. C. Leon, T. McCarry, M. Nurse, T. A. Hare, et al., "The NimStim set of facial expressions: judgments from untrained research participants," Psychiatry Research, vol. 168, no. 3, pp. 242-249, 2009.
DOI
|
21 |
V. Smith, C. K. Chiang, M. Sanjabi, and A. Talwalkar, "Federated multi-task learning," 2017 [Online]. Available: https://arxiv.org/abs/1705.10467.
|
22 |
C. Vens, J. Struyf, L. Schietgat, S. Dzeroski, and H. Blockeel, "Decision trees for hierarchical multi-label classification," Machine Learning, vol. 73, no. 2, pp. 185-214, 2008.
DOI
|
23 |
R. G. Brereton and G. R. Lloyd, "Support vector machines for classification and regression," Analyst, vol. 135, no. 2, pp. 230-267, 2010.
DOI
|
24 |
Y. Wang, M. Nikkhah, X. Zhu, W. T. Tan, and R. Liston, "Learning geographically distributed data for multiple tasks using generative adversarial networks," in Proceedings of 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 4589-4593.
|
25 |
H. Brendan McMahan, E. Moore, D. Ramage, and B. A. Y. Arcas, "Federated learning of deep networks using model averaging," 2016 [Online]. Available: https://arxiv.org/abs/1602.05629v1
|
26 |
Y. Xue, X. Liao, L. Carin, and B. Krishnapuram, "Multi-task learning for classification with Dirichlet process priors," Journal of Machine Learning Research, vol. 8, pp. 35-63, 2007.
|
27 |
S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
DOI
|
28 |
X. T. Yuan, X. Liu, and S. Yan, "Visual classification with multitask joint sparse representation," IEEE Transactions on Image Processing, vol. 21, no. 10, pp. 4349-4360, 2012.
DOI
|
29 |
L. Argote and E. Miron-Spektor, "Organizational learning: from experience to knowledge," Organization Science, vol. 22, no. 5, pp. 1123-1137, 2011.
DOI
|
30 |
S. Rosen, Z. Qian, and Z. M. Mao, "Appprofiler: a flexible method of exposing privacy-related behavior in android applications to end users," in Proceedings of the 3rd ACM Conference on Data and Application Security and Privacy, San Antonio, TX, 2013, pp. 221-232.
|