  Providing generalization guarantees for modern neural networks has been a
crucial task in statistical learning. Recently, several studies have attempted
to analyze the generalization error in such settings by using tools from
fractal geometry. While these works have successfully introduced new
mathematical tools to apprehend generalization, they heavily rely on a
Lipschitz continuity assumption, which in general does not hold for neural
networks and might make the bounds vacuous. In this work, we address this issue
and prove fractal geometry-based generalization bounds without requiring any
Lipschitz assumption. To achieve this goal, we build up on a classical covering
argument in learning theory and introduce a data-dependent fractal dimension.
Despite introducing a significant amount of technical complications, this new
notion lets us control the generalization error (over either fixed or random
hypothesis spaces) along with certain mutual information (MI) terms. To provide
a clearer interpretation to the newly introduced MI terms, as a next step, we
introduce a notion of "geometric stability" and link our bounds to the prior
art. Finally, we make a rigorous connection between the proposed data-dependent
dimension and topological data analysis tools, which then enables us to compute
the dimension in a numerically efficient way. We support our theory with
experiments conducted on various settings.
