Bounding the expectation of the supremum of empirical processes indexed by H"older classes
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Abstract (arXiv)
In this note, we provide upper bounds on the expectation of the supremum of empirical processes indexed by H"older classes of any smoothness and for any distribution supported on a bounded set in mathbb Rd. These results can be alternatively seen as non-asymptotic risk bounds, when the unknown distribution is estimated by its empirical counterpart, based on n independent observations, and the error of estimation is quantified by the integral probability metrics (IPM). In particular, the IPM indexed by a H"older class is considered and the corresponding rates are derived. These results interpolate between the two well-known extreme cases: the rate n-1/d corresponding to the Wassertein-1 distance (the least smooth case) and the fast rate n-1/2 corresponding to very smooth functions (for instance, functions from an RKHS defined by a bounded kernel).
Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). First posted on arXiv 30 Mar 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 1; 2025: 2.
8 papers.
- 2021A likelihood approach to nonparametric estimation of a singular distribution using deep generative modelscited by 12
- 2025On the minimax optimality of Flow Matching through the connection to kernel density estimationcited by 9
- 2022Imaging Conductivity from Current Density Magnitude using Neural Networkscited by 6
- 2022GAN Estimation of Lipschitz Optimal Transport Mapscited by 4
- 2023Near-optimal learning with average H"older smoothnesscited by 4
- 2021Non-Asymptotic Error Bounds for Bidirectional GANscited by 3
- 2024A Wasserstein perspective of Vanilla GANscited by 3
- 2025Distributional Shrinkage II: Higher-Order Scores Encode Brenier Mapcited by 0
- 2025Distributional Shrinkage II: Higher-Order Scores Encode Brenier Map10 Dec 2025
- 2025On the minimax optimality of Flow Matching through the connection to kernel density estimation17 Apr 2025
- 2024A Wasserstein perspective of Vanilla GANs22 Mar 2024
- 2023Near-optimal learning with average H"older smoothness12 Feb 2023
- 2022Imaging Conductivity from Current Density Magnitude using Neural Networks5 Apr 2022
- 2022GAN Estimation of Lipschitz Optimal Transport Maps16 Feb 2022
- 2021Non-Asymptotic Error Bounds for Bidirectional GANs24 Oct 2021
- 2021A likelihood approach to nonparametric estimation of a singular distribution using deep generative models9 May 2021