  The solution to empirical risk minimization with $f$-divergence
regularization (ERM-$f$DR) is presented under mild conditions on $f$. Under
such conditions, the optimal measure is shown to be unique. Examples of the
solution for particular choices of the function $f$ are presented. Previously
known solutions to common regularization choices are obtained by leveraging the
flexibility of the family of $f$-divergences. These include the unique
solutions to empirical risk minimization with relative entropy regularization
(Type-I and Type-II). The analysis of the solution unveils the following
properties of $f$-divergences when used in the ERM-$f$DR problem: $i\bigl)$
$f$-divergence regularization forces the support of the solution to coincide
with the support of the reference measure, which introduces a strong inductive
bias that dominates the evidence provided by the training data; and $ii\bigl)$
any $f$-divergence regularization is equivalent to a different $f$-divergence
regularization with an appropriate transformation of the empirical risk
function.
