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Integrating low-rank and group-sparse structures for robust multi-task learning
2011
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11
Multi-task learning (MTL) aims at improving the generalization performance by utilizing the intrinsic relationships among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not be the case in many realworld applications. In this paper, we propose a robust multi-task learning (RMTL) algorithm which learns multiple tasks simultaneously as well as identifies the irrelevant (outlier) tasks. Specifically, the proposed RMTL algorithm
doi:10.1145/2020408.2020423
dblp:conf/kdd/ChenZY11
fatcat:l6ntfkv52be5fhwooiigd2cu7y