The VC-dimension and point configurations in $\mathbb{R}^d$
Abstract
Given a set and a collection of functions from to , the VC-dimension measures the complexity of the hypothesis class in the context of PAC learning. In recent years, this has been connected to geometric configuration problems in vector spaces over finite fields. In particular, it is easy to show that the VC-dimension of the set of spheres of a given radius in is equal to , since this is how many points generically determine a sphere. It is known that for , , the set of spheres centered at points in , and intersected with the set , has VC-dimension either or . In this paper, we study a similar question over Euclidean space. We find an explicit dimensional threshold so that whenever , , and the Hausdorff dimension of is at least , it follows that there exists an interval such that for any , the VC-dimension of the set of spheres of radius centered at points in , and intersected with , is at least . In the process of proving this theorem, we also provide the first explicit dimensional threshold for a set to contain a -cycle, i.e. satisfying
Keywords
Cite
@article{arxiv.2510.13984,
title = {The VC-dimension and point configurations in $\mathbb{R}^d$},
author = {Alex Iosevich and Akos Magyar and Alex McDonald and Brian McDonald},
journal= {arXiv preprint arXiv:2510.13984},
year = {2025}
}