Uniform concentration and symmetrization for weak interactions
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Abstract (arXiv)
The method to derive uniform bounds with Gaussian and Rademacher complexities is extended to the case where the sample average is replaced by a nonlinear statistic. Tight bounds are obtained for U-statistics, smoothened L-statistics and error functionals of l2-regularized algorithms.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Feb 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2021On the Role of Neural Collapse in Transfer Learningcited by 100
- 2022Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifierscited by 7
- 2025One-shot Conditional Sampling: MMD meets Nearest Neighborscited by 0
- 2026Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Testscited by 0
- 2026Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests7 May 2026
- 2025One-shot Conditional Sampling: MMD meets Nearest Neighbors29 Sep 2025
- 2022Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers23 Dec 2022
- 2021On the Role of Neural Collapse in Transfer Learning30 Dec 2021