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Research PaperResearchia:202607.23004

Lipschitzian SLLNs for random functions

Lai Tian

Abstract

We prove strong laws of large numbers for locally Lipschitz functions in the Lipschitz pseudometric. Our results hold under either a topological or a model-theoretic condition, with the latter encompassing functions jointly definable in o-minimal structures but extending substantially beyond this class. Applications include uniform convergence of limiting and Clarke subdifferentials and finite-sample identification of solutions. Consequently, we identify broad classes of functions for which the ...

Submitted: July 23, 2026Subjects: Machine Learning; Data Science

Description / Details

We prove strong laws of large numbers for locally Lipschitz functions in the Lipschitz pseudometric. Our results hold under either a topological or a model-theoretic condition, with the latter encompassing functions jointly definable in o-minimal structures but extending substantially beyond this class. Applications include uniform convergence of limiting and Clarke subdifferentials and finite-sample identification of solutions. Consequently, we identify broad classes of functions for which the failure phenomena revealed by our previous negative results [Tian and Royset, arXiv:2511.16568, 2025] do not occur.


Source: arXiv:2607.20411v1 - http://arxiv.org/abs/2607.20411v1 PDF: https://arxiv.org/pdf/2607.20411v1 Original Link: http://arxiv.org/abs/2607.20411v1

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Submission Info
Date:
Jul 23, 2026
Topic:
Data Science
Area:
Machine Learning
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