Near-tight closure bounds for Littlestone and threshold dimensions
Machine Learning
2020-07-08 v1 Combinatorics
Machine Learning
Abstract
We study closure properties for the Littlestone and threshold dimensions of binary hypothesis classes. Given classes of Boolean functions with bounded Littlestone (respectively, threshold) dimension, we establish an upper bound on the Littlestone (respectively, threshold) dimension of the class defined by applying an arbitrary binary aggregation rule to . We also show that our upper bounds are nearly tight. Our upper bounds give an exponential (in ) improvement upon analogous bounds shown by Alon et al. (COLT 2020), thus answering a question posed by their work.
Cite
@article{arxiv.2007.03668,
title = {Near-tight closure bounds for Littlestone and threshold dimensions},
author = {Badih Ghazi and Noah Golowich and Ravi Kumar and Pasin Manurangsi},
journal= {arXiv preprint arXiv:2007.03668},
year = {2020}
}
Comments
7 pages