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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.08223 |
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| _version_ | 1866916566318186496 |
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| author | Ober, Sebastian W. Power, Samuel Diethe, Tom Moss, Henry B. |
| author_facet | Ober, Sebastian W. Power, Samuel Diethe, Tom Moss, Henry B. |
| contents | We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to suboptimal performance. Motivated by this observation, we propose to focus on the predictive probabilities, which only exhibit epistemic uncertainty. The result is an acquisition function that not only performs better, but is also faster to evaluate, allowing for larger batches than before. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08223 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Big Batch Bayesian Active Learning by Considering Predictive Probabilities Ober, Sebastian W. Power, Samuel Diethe, Tom Moss, Henry B. Machine Learning We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to suboptimal performance. Motivated by this observation, we propose to focus on the predictive probabilities, which only exhibit epistemic uncertainty. The result is an acquisition function that not only performs better, but is also faster to evaluate, allowing for larger batches than before. |
| title | Big Batch Bayesian Active Learning by Considering Predictive Probabilities |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.08223 |