Batch Active Learning in Gaussian Process Regression using Derivatives
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910554660012032 |
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| author | Yu, Hon Sum Alec Zimmer, Christoph Nguyen-Tuong, Duy |
| author_facet | Yu, Hon Sum Alec Zimmer, Christoph Nguyen-Tuong, Duy |
| contents | We investigate the use of derivative information for Batch Active Learning in Gaussian Process regression models. The proposed approach employs the predictive covariance matrix for selection of data batches to exploit full correlation of samples. We theoretically analyse our proposed algorithm taking different optimality criteria into consideration and provide empirical comparisons highlighting the advantage of incorporating derivatives information. Our results show the effectiveness of our approach across diverse applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Batch Active Learning in Gaussian Process Regression using Derivatives Yu, Hon Sum Alec Zimmer, Christoph Nguyen-Tuong, Duy Machine Learning We investigate the use of derivative information for Batch Active Learning in Gaussian Process regression models. The proposed approach employs the predictive covariance matrix for selection of data batches to exploit full correlation of samples. We theoretically analyse our proposed algorithm taking different optimality criteria into consideration and provide empirical comparisons highlighting the advantage of incorporating derivatives information. Our results show the effectiveness of our approach across diverse applications. |
| title | Batch Active Learning in Gaussian Process Regression using Derivatives |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.01861 |