Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique
Computational Complexity
2022-11-04 v1 Machine Learning
Abstract
We study the complexity of computing (and approximating) VC Dimension and Littlestone's Dimension when we are given the concept class explicitly. We give a simple reduction from Maximum (Unbalanced) Biclique problem to approximating VC Dimension and Littlestone's Dimension. With this connection, we derive a range of hardness of approximation results and running time lower bounds. For example, under the (randomized) Gap-Exponential Time Hypothesis or the Strongish Planted Clique Hypothesis, we show a tight inapproximability result: both dimensions are hard to approximate to within a factor of in polynomial-time. These improve upon constant-factor inapproximability results from [Manurangsi and Rubinstein, COLT 2017].
Keywords
Cite
@article{arxiv.2211.01443,
title = {Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique},
author = {Pasin Manurangsi},
journal= {arXiv preprint arXiv:2211.01443},
year = {2022}
}
Comments
To appear in ITCS 2023