%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 }