图 书 馆
校准多任务学习Calibrated Multi‑Task Learning |
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| 课程网址: | http://videolectures.net/kdd2018_hu_calibrated_learning/ |
| 主讲教师: | Zhanxuan Hu |
| 开课单位: | 西北工业大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This 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 nonsmooth loss function and a non-convex regularization term, we construct an efcient re-weighted method to optimize it. Theoretically, we frst 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. |
| 关 键 词: | 非凸校准多任务学习算法; 联合学习多个相关回归任务; 有效的加权方法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-02-03：cyh |
| 最后编审: | 2023-02-03：cyh |
| 阅读次数: | 34 |