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arXiv Machine Learning By Behrooz Tahmasebi, Melanie Weber

Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation

Read the original on arXiv Machine Learning →

The paper introduces a statistical learning theory for size generalization in models that handle variable-size structured data such as sets and graphs. It proves an exponential separation in sample complexity: polynomial in-domain learning yields only logarithmic decay in size-generalization error, while an exponential source condition restores polynomial decay. These results provide dimension‑free scaling laws and optimal minimax bounds for transferring learning across input sizes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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