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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.02473 |
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| _version_ | 1866911778006368256 |
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| author | Hosseini, Sepidehsadat Zhai, Mengyao Hajimirsadegh, Hossein Tung, Frederick |
| author_facet | Hosseini, Sepidehsadat Zhai, Mengyao Hajimirsadegh, Hossein Tung, Frederick |
| contents | Machine learning traditionally assumes that the training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution can shift over time, leading to poor generalization of trained models in future time periods. This paper presents a novel prompting-based approach to temporal domain generalization that is parameter-efficient, time-efficient, and does not require access to future data during training. Our method adapts a trained model to temporal drift by learning global prompts, domain-specific prompts, and drift-aware prompts that capture underlying temporal dynamics. Experiments on classification, regression, and time series forecasting tasks demonstrate the generality of the proposed approach. The code repository will be publicly shared. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02473 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Prompting-based Temporal Domain Generalization Hosseini, Sepidehsadat Zhai, Mengyao Hajimirsadegh, Hossein Tung, Frederick Machine Learning Machine learning traditionally assumes that the training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution can shift over time, leading to poor generalization of trained models in future time periods. This paper presents a novel prompting-based approach to temporal domain generalization that is parameter-efficient, time-efficient, and does not require access to future data during training. Our method adapts a trained model to temporal drift by learning global prompts, domain-specific prompts, and drift-aware prompts that capture underlying temporal dynamics. Experiments on classification, regression, and time series forecasting tasks demonstrate the generality of the proposed approach. The code repository will be publicly shared. |
| title | Prompting-based Temporal Domain Generalization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.02473 |