  In this work, we unify several expected generalization error bounds based on
random subsets using the framework developed by Hellstr\"om and Durisi [1].
First, we recover the bounds based on the individual sample mutual information
from Bu et al. [2] and on a random subset of the dataset from Negrea et al.
[3]. Then, we introduce their new, analogous bounds in the randomized subsample
setting from Steinke and Zakynthinou [4], and we identify some limitations of
the framework. Finally, we extend the bounds from Haghifam et al. [5] for
Langevin dynamics to stochastic gradient Langevin dynamics and we refine them
for loss functions with potentially large gradient norms.
