arXiv Machine Learning June 2, 2026 By Andreas Maurer, Erfan Mirzaei, Massimiliano Pontil

Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime

Read the original on arXiv Machine Learning →

arXiv:2510. 06028v3 Announce Type: replace Abstract: This paper provides data-dependent bounds on the expected error of the Gibbs algorithm in the overparameterized interpolation regime, where low training errors are also obtained for impossible data, such as random labels in classification.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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