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Bounding the expectation of the supremum of empirical processes indexed
  by Hölder classes
v1v2v3 (latest)

Bounding the expectation of the supremum of empirical processes indexed by Hölder classes

Mathematical Methods of Statistics (MMS), 2020
30 March 2020
Nicolas Schreuder
ArXiv (abs)PDFHTML

Papers citing "Bounding the expectation of the supremum of empirical processes indexed by Hölder classes"

10 / 10 papers shown
On the minimax optimality of Flow Matching through the connection to kernel density estimation
On the minimax optimality of Flow Matching through the connection to kernel density estimation
Lea Kunkel
Mathias Trabs
355
6
0
17 Apr 2025
A Wasserstein perspective of Vanilla GANs
A Wasserstein perspective of Vanilla GANsNeural Networks (NN), 2024
Lea Kunkel
Mathias Trabs
263
17
0
22 Mar 2024
Near-optimal learning with average Hölder smoothness
Near-optimal learning with average Hölder smoothnessNeural Information Processing Systems (NeurIPS), 2023
Steve Hanneke
A. Kontorovich
Guy Kornowski
289
5
0
12 Feb 2023
Imaging Conductivity from Current Density Magnitude using Neural
  Networks
Imaging Conductivity from Current Density Magnitude using Neural NetworksInverse Problems (IP), 2022
Bangti Jin
Xiyao Li
Xiliang Lu
271
17
0
05 Apr 2022
GAN Estimation of Lipschitz Optimal Transport Maps
GAN Estimation of Lipschitz Optimal Transport Maps
Alberto González Sanz
Lucas de Lara
Louis Bethune
Jean-Michel Loubes
OT
179
6
0
16 Feb 2022
Rates of convergence for nonparametric estimation of singular
  distributions using generative adversarial networks
Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networksJournal of the Korean Statistical Society (JKSS), 2022
Minwoo Chae
GAN
320
6
0
07 Feb 2022
Non-Asymptotic Error Bounds for Bidirectional GANs
Non-Asymptotic Error Bounds for Bidirectional GANsNeural Information Processing Systems (NeurIPS), 2021
Shiao Liu
Yunfei Yang
Jian Huang
Yuling Jiao
Yang Wang
217
8
0
24 Oct 2021
A likelihood approach to nonparametric estimation of a singular
  distribution using deep generative models
A likelihood approach to nonparametric estimation of a singular distribution using deep generative modelsJournal of machine learning research (JMLR), 2021
Minwoo Chae
Dongha Kim
Yongdai Kim
Lizhen Lin
632
23
0
09 May 2021
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank
  Tensor Completion and Tensor Robust Principal Component Analysis
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
Jicong Fan
Lijun Ding
Chengrun Yang
Zhao Zhang
Madeleine Udell
972
7
0
07 Dec 2020
Statistical guarantees for generative models without domination
Statistical guarantees for generative models without dominationInternational Conference on Algorithmic Learning Theory (ALT), 2020
Nicolas Schreuder
Victor-Emmanuel Brunel
A. Dalalyan
GAN
264
39
0
19 Oct 2020
1
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