Fat-Shattering Dimension of K-Fold Aggregations

Abstract

We provide estimates on the fat-shattering dimension of aggregation rules of real-valued function classes. The latter consists of all ways of choosing k functions, one from each of the k classes, and computing pointwise an "aggregate" function of these, such as the median, mean, and maximum. The bounds are stated in terms of the fat-shattering dimensions of the component classes. For linear and affine function classes, we provide a considerably sharper upper bound and a matching lower bound, achieving, in particular, an optimal dependence on k. Along the way, we improve several known results in addition to pointing out and correcting a number of erroneous claims in the literature.

Cite

Text

Attias and Kontorovich. "Fat-Shattering Dimension of K-Fold Aggregations." Journal of Machine Learning Research, 2024.

Markdown

[Attias and Kontorovich. "Fat-Shattering Dimension of K-Fold Aggregations." Journal of Machine Learning Research, 2024.](https://mlanthology.org/jmlr/2024/attias2024jmlr-fatshattering/)

BibTeX

@article{attias2024jmlr-fatshattering,
  title     = {{Fat-Shattering Dimension of K-Fold Aggregations}},
  author    = {Attias, Idan and Kontorovich, Aryeh},
  journal   = {Journal of Machine Learning Research},
  year      = {2024},
  pages     = {1-29},
  volume    = {25},
  url       = {https://mlanthology.org/jmlr/2024/attias2024jmlr-fatshattering/}
}