Rejection via Learning Density Ratios
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915276614795264 |
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| author | Soen, Alexander Husain, Hisham Schulz, Philip Nguyen, Vu |
| author_facet | Soen, Alexander Husain, Hisham Schulz, Philip Nguyen, Vu |
| contents | Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. The predominant approach is to alter the supervised learning pipeline by augmenting typical loss functions, letting model rejection incur a lower loss than an incorrect prediction. Instead, we propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. This can be formalized via the optimization of a loss's risk with a $φ$-divergence regularization term. Through this idealized distribution, a rejection decision can be made by utilizing the density ratio between this distribution and the data distribution. We focus on the setting where our $φ$-divergences are specified by the family of $α$-divergence. Our framework is tested empirically over clean and noisy datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rejection via Learning Density Ratios Soen, Alexander Husain, Hisham Schulz, Philip Nguyen, Vu Machine Learning Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. The predominant approach is to alter the supervised learning pipeline by augmenting typical loss functions, letting model rejection incur a lower loss than an incorrect prediction. Instead, we propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. This can be formalized via the optimization of a loss's risk with a $φ$-divergence regularization term. Through this idealized distribution, a rejection decision can be made by utilizing the density ratio between this distribution and the data distribution. We focus on the setting where our $φ$-divergences are specified by the family of $α$-divergence. Our framework is tested empirically over clean and noisy datasets. |
| title | Rejection via Learning Density Ratios |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.18686 |