Domain Generalization Under Posterior Drift
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910011404320768 |
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| author | Zhu, Yilun Deng, Naihao Shi, Naichen Gangrade, Aditya Scott, Clayton |
| author_facet | Zhu, Yilun Deng, Naihao Shi, Naichen Gangrade, Aditya Scott, Clayton |
| contents | Domain generalization (DG) is the problem of generalizing from several distributions (or domains), for which labeled training data are available, to a new test domain for which no labeled data is available. For the prevailing benchmark datasets in DG, there exists a single classifier that performs well across all domains.
In this work, we study a fundamentally different regime where the domains satisfy a \emph{posterior drift} assumption, in which the optimal classifier might vary substantially with domain. We establish a decision-theoretic framework for DG under posterior drift, and investigate the practical implications of this framework through experiments on language and vision tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain Generalization Under Posterior Drift Zhu, Yilun Deng, Naihao Shi, Naichen Gangrade, Aditya Scott, Clayton Machine Learning Domain generalization (DG) is the problem of generalizing from several distributions (or domains), for which labeled training data are available, to a new test domain for which no labeled data is available. For the prevailing benchmark datasets in DG, there exists a single classifier that performs well across all domains. In this work, we study a fundamentally different regime where the domains satisfy a \emph{posterior drift} assumption, in which the optimal classifier might vary substantially with domain. We establish a decision-theoretic framework for DG under posterior drift, and investigate the practical implications of this framework through experiments on language and vision tasks. |
| title | Domain Generalization Under Posterior Drift |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.04441 |