English

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 (x,C)(x,C)-th entry is 11 iff element xx belongs to concept CC), both can be computed exactly in quasi-polynomial time (nO(logn)n^{O(\log n)}). 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