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De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and
  Non-smooth Predictors
v1v2v3 (latest)

De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictors

23 February 2020
A. Banerjee
Tiancong Chen
Yingxue Zhou
    BDL
ArXiv (abs)PDFHTML

Papers citing "De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictors"

6 / 6 papers shown
Sparsity-aware generalization theory for deep neural networks
Sparsity-aware generalization theory for deep neural networksAnnual Conference Computational Learning Theory (COLT), 2023
Ramchandran Muthukumar
Jeremias Sulam
MLT
370
8
0
01 Jul 2023
Generalisation under gradient descent via deterministic PAC-Bayes
Generalisation under gradient descent via deterministic PAC-BayesInternational Conference on Algorithmic Learning Theory (ALT), 2022
Eugenio Clerico
Tyler Farghly
George Deligiannidis
Benjamin Guedj
Arnaud Doucet
501
6
0
06 Sep 2022
Adversarial robustness of sparse local Lipschitz predictors
Adversarial robustness of sparse local Lipschitz predictorsSIAM Journal on Mathematics of Data Science (SIMODS), 2022
Ramchandran Muthukumar
Jeremias Sulam
AAML
286
16
0
26 Feb 2022
On Margins and Derandomisation in PAC-Bayes
On Margins and Derandomisation in PAC-BayesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Felix Biggs
Benjamin Guedj
347
23
0
08 Jul 2021
Risk Bounds for Learning via Hilbert Coresets
Risk Bounds for Learning via Hilbert Coresets
Spencer Douglas
Piyush Kumar
R. Prasanth
203
0
0
29 Mar 2021
Generalization bounds for deep learning
Generalization bounds for deep learning
Guillermo Valle Pérez
A. Louis
BDL
331
48
0
07 Dec 2020
1
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