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arXiv Machine Learning By Qianqian Lei, Soham Bonnerjee, Yuefeng Han, Wei Biao Wu

Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite $L_p$ Moments

Read the original on arXiv Machine Learning →

arXiv:2606. 06855v1 Announce Type: cross Abstract: While algorithmic stability is a central tool for understanding generalization of learning algorithms, existing high-probability guarantees typically rely on uniform boundedness or sub-Gaussian/sub-Weibull tail assumptions, which can be overly restrictive for modern settings with heavy-tailed or unbounded losses.

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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