Inapproximability of VC Dimension and Littlestone's Dimension
Computational Complexity
2017-05-29 v1
Abstract
We study the complexity of computing the VC Dimension and Littlestone's Dimension. Given an explicit description of a finite universe and a concept class (a binary matrix whose -th entry is iff element belongs to concept ), both can be computed exactly in quasi-polynomial time (). Assuming the randomized Exponential Time Hypothesis (ETH), we prove nearly matching lower bounds on the running time, that hold even for approximation algorithms.
Keywords
Cite
@article{arxiv.1705.09517,
title = {Inapproximability of VC Dimension and Littlestone's Dimension},
author = {Pasin Manurangsi and Aviad Rubinstein},
journal= {arXiv preprint arXiv:1705.09517},
year = {2017}
}
Comments
To appear in Conference On Learning Theory (COLT) 2017