Zhou, M., Zhang, Y., Yang, Y. et al. (2 more authors) (2023) Robust temporal smoothness in multi-task learning. In: Williams, B., Chen, Y. and Neville, J., (eds.) Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. Thirty-Seventh AAAI Conference on Artificial Intelligence, 07-14 Feb 2023, Washington DC, USA. Association for the Advancement of Artificial Intelligence (AAAI) , pp. 11426-11434. ISBN 9781577358800
Abstract
Multi-task learning models based on temporal smoothness assumption, in which each time point of a sequence of time points concerns a task of prediction, assume the adjacent tasks are similar to each other. However, the effect of outliers is not taken into account. In this paper, we show that even only one outlier task will destroy the performance of the entire model. To solve this problem, we propose two Robust Temporal Smoothness (RoTS) frameworks. Compared with the existing models based on temporal relation, our methods not only chase the temporal smoothness information but identify outlier tasks, however, without increasing the computational complexity. Detailed theoretical analyses are presented to evaluate the performance of our methods. Experimental results on synthetic and real-life datasets demonstrate the effectiveness of our frameworks. We also discuss several potential specific applications and extensions of our RoTS frameworks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023, Association for the Advancement of Artificial Intelligence. |
Keywords: | ML: Transfer; Domain Adaptation; Multi-Task Learning; DMKM: Data Stream Mining; DMKM: Mining of Spatial; Temporal or Spatio-Temporal Data; ML: Optimization; ML: Transparent; Interpretable; Explainable ML |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Feb 2024 11:46 |
Last Modified: | 08 Feb 2024 11:47 |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence (AAAI) |
Refereed: | Yes |
Identification Number: | 10.1609/aaai.v37i9.26351 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208826 |