Fat-Shattering Dimension of k-fold Aggregations
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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
- 2021A Theory of PAC Learnability of Partial Concept Classescited by 59
- 2018Improved Generalization Bounds for Adversarially Robust Learningcited by 22
- 2020Closure Properties for Private Classification and Online Predictioncited by 15
- 2020Robust k-means Clustering for Distributions with Two Momentscited by 14
- 2013Efficient Classification for Metric Datacited by 9
- 2020Near-tight closure bounds for Littlestone and threshold dimensionscited by 7
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Citing papers per year: 2024: 1; 2025: 1; 2026: 3.
5 papers.
- 2026Coverage Improvement and Fast Convergence of On-policy Preference Learningcited by 5
- 2024Learning With Multi-Group Guarantees For Clusterable Subpopulationscited by 3
- 2026Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scalecited by 1
- 2025Transductive and Learning-Augmented Online Regressioncited by 0
- 2026The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexitycited by 0
- 2026The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity28 May 2026
- 2026Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale13 May 2026
- 2026Coverage Improvement and Fast Convergence of On-policy Preference Learning13 Jan 2026
- 2025Transductive and Learning-Augmented Online Regression4 Oct 2025
- 2024Learning With Multi-Group Guarantees For Clusterable Subpopulations18 Oct 2024