Generalization bound for parameters rather than loss functions
See our recent paper:
&quot;Non-parametric Binary regression in metric spaces with KL loss&quot;
https://arxiv.org/abs/2010.09886
The setting is actually non-parametric and quite general. Our loss measures the expected log-likelihood -- which is more natural in the context of logistic regression than distance in parameter space. Notice that our loss is unbounded, as would be the $\ell_1$ or $\ell_2$ loss in parameter space.