Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909146665713664 |
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| author | Bobbia, Benjamin Picard, Matthias |
| author_facet | Bobbia, Benjamin Picard, Matthias |
| contents | This paper addresses a new active learning strategy for regression problems. The presented Wasserstein active regression model is based on the principles of distribution-matching to measure the representativeness of the labeled dataset. The Wasserstein distance is computed using GroupSort Neural Networks. The use of such networks provides theoretical foundations giving a way to quantify errors with explicit bounds for their size and depth. This solution is combined with another uncertainty-based approach that is more outlier-tolerant to complete the query strategy. Finally, this method is compared with other classical and recent solutions. The study empirically shows the pertinence of such a representativity-uncertainty approach, which provides good estimation all along the query procedure. Moreover, the Wasserstein active regression often achieves more precise estimations and tends to improve accuracy faster than other models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks Bobbia, Benjamin Picard, Matthias Machine Learning Statistics Theory This paper addresses a new active learning strategy for regression problems. The presented Wasserstein active regression model is based on the principles of distribution-matching to measure the representativeness of the labeled dataset. The Wasserstein distance is computed using GroupSort Neural Networks. The use of such networks provides theoretical foundations giving a way to quantify errors with explicit bounds for their size and depth. This solution is combined with another uncertainty-based approach that is more outlier-tolerant to complete the query strategy. Finally, this method is compared with other classical and recent solutions. The study empirically shows the pertinence of such a representativity-uncertainty approach, which provides good estimation all along the query procedure. Moreover, the Wasserstein active regression often achieves more precise estimations and tends to improve accuracy faster than other models. |
| title | Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2403.15108 |