mlsanityData to 7 Oct 2026

Domain Generalization Under Posterior Drift

arXiv 2510.04441 ↗First posted 6 Oct 2025cs.LG stat.MLcited by 2

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Abstract (arXiv)

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 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 44 of its 53 references that are in the map. First posted on arXiv 6 Oct 2025.

Builds on

44 of 53 references.

References · 44

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32 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

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