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PAC-Bayesian Bounds based on the Rényi Divergence
Abstract : We propose a simplified proof process for PAC-Bayesian generalization bounds, that allows to divide the proof in four successive inequalities, easing the "customization" of PAC-Bayesian theorems. We also propose a family of PAC-Bayesian bounds based on the Rényi divergence between the prior and posterior distributions, whereas most PAC-Bayesian bounds are based on the Kullback-Leibler divergence. Finally, we present an empirical evaluation of the tightness of each inequality of the simplified proof, for both the classical PAC-Bayesian bounds and those based on the Rényi divergence.
Keywords :
Statistical Learning Theory
PAC-Bayes
Cited literature [31 references]
https://hal.inria.fr/hal-01384783
Contributor : Pascal Germain Connect in order to contact the contributor
Submitted on : Thursday, October 20, 2016 - 3:07:50 PM
Last modification on : Thursday, March 17, 2022 - 10:08:52 AM
Contributor : Pascal Germain Connect in order to contact the contributor
Submitted on : Thursday, October 20, 2016 - 3:07:50 PM
Last modification on : Thursday, March 17, 2022 - 10:08:52 AM
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