mlsanityData to 7 Oct 2026

Fat-Shattering Dimension of k-fold Aggregations

arXiv 2110.04763 ↗First posted 10 Oct 2021math.FA cs.LG stat.MLcited by 5

Not on the map. Why?

Abstract (arXiv)

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 a pointwise function of them, such as the median, mean, and maximum. The bound is 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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 46 references that are in the map. First posted on arXiv 10 Oct 2021.

Builds on

6 of 46 references.

References · 6

  1. 2021A Theory of PAC Learnability of Partial Concept Classescited by 59
  2. 2018Improved Generalization Bounds for Adversarially Robust Learningcited by 22
  3. 2020Closure Properties for Private Classification and Online Predictioncited by 15
  4. 2020Robust k-means Clustering for Distributions with Two Momentscited by 14
  5. 2013Efficient Classification for Metric Datacited by 9
  6. 2020Near-tight closure bounds for Littlestone and threshold dimensionscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.