aiqbench analysis
This research introduces hierarchical domain generalization, addressing the challenge of extrapolating from limited observed regions to broader instance spaces without relying on independent and identically distributed sampling. The authors demonstrate that train/test partitioning is a critical factor in generalization failure, suggesting future theories must treat domain structure as fundamental rather than incidental. These findings advance machine learning theory by redefining how models handle arbitrary domain hierarchies beyond standard i.i.d. assumptions.
Hierarchical Domain Generalization
arXiv:2607.16528v1 Announce Type: new Abstract: We study hierarchical domain generalization as a problem of extrapolation from finite observed regions to an entire instance space, replacing i.i.d. sampling with arbitrary domain hierarchies. We show that the central obstruction is not only the complexity of the hypothesis class, but the train/test domain partition through which evidence is revealed. In particular, no matter how small the class or how large the training size, some partition makes generalization fail for some target. These results suggest that modern generalization theory must treat domain structure as a first-class object.
Abstract:We study hierarchical domain generalization as a problem of extrapolation from finite observed regions to an entire instance space, replacing i.i.d. sampling with arbitrary domain hierarchies. We show that the central obstruction is not only the complexity of the hypothesis class, but the train/test domain partition through which evidence is revealed. In particular, no matter how small the class or how large the training size, some partition makes generalization fail for some target. These results suggest that modern generalization theory must treat domain structure as a first-class object.
Machine Learning (cs.LG)
arXiv:2607.16528 [cs.LG]
(or arXiv:2607.16528v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.16528 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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