When I first encountered PAC-Bayesian concentration inequalities they seemed
to me to be rather disconnected from good old-fashioned results like
Hoeffding's and Bernstein's inequalities. But, at least for one flavour of the
PAC-Bayesian bounds, there is actually a very close relation, and the main
innovation is a continuous version of the union bound, along with some
ingenious applications. Here's the gist of what's going on, presented from a
machine learning perspective.
Description
[1405.1580] PAC-Bayes Mini-tutorial: A Continuous Union Bound
%0 Generic
%1 vanerven2014pacbayes
%A van Erven, Tim
%D 2014
%K bounds complexity generalization readings tutorials
%T PAC-Bayes Mini-tutorial: A Continuous Union Bound
%U http://arxiv.org/abs/1405.1580
%X When I first encountered PAC-Bayesian concentration inequalities they seemed
to me to be rather disconnected from good old-fashioned results like
Hoeffding's and Bernstein's inequalities. But, at least for one flavour of the
PAC-Bayesian bounds, there is actually a very close relation, and the main
innovation is a continuous version of the union bound, along with some
ingenious applications. Here's the gist of what's going on, presented from a
machine learning perspective.
@conference{vanerven2014pacbayes,
abstract = {When I first encountered PAC-Bayesian concentration inequalities they seemed
to me to be rather disconnected from good old-fashioned results like
Hoeffding's and Bernstein's inequalities. But, at least for one flavour of the
PAC-Bayesian bounds, there is actually a very close relation, and the main
innovation is a continuous version of the union bound, along with some
ingenious applications. Here's the gist of what's going on, presented from a
machine learning perspective.},
added-at = {2019-11-20T15:22:56.000+0100},
author = {van Erven, Tim},
biburl = {https://www.bibsonomy.org/bibtex/2811d2881f9944b98f1ac3f99eac16415/kirk86},
description = {[1405.1580] PAC-Bayes Mini-tutorial: A Continuous Union Bound},
interhash = {a5dbbc33cb440fbbdbae72e83a8effeb},
intrahash = {811d2881f9944b98f1ac3f99eac16415},
keywords = {bounds complexity generalization readings tutorials},
note = {cite arxiv:1405.1580},
timestamp = {2019-11-20T15:22:56.000+0100},
title = {PAC-Bayes Mini-tutorial: A Continuous Union Bound},
url = {http://arxiv.org/abs/1405.1580},
year = 2014
}