  We present a new framework for deriving bounds on the generalization bound of
statistical learning algorithms from the perspective of online learning.
Specifically, we construct an online learning game called the "generalization
game", where an online learner is trying to compete with a fixed statistical
learning algorithm in predicting the sequence of generalization gaps on a
training set of i.i.d. data points. We establish a connection between the
online and statistical learning setting by showing that the existence of an
online learning algorithm with bounded regret in this game implies a bound on
the generalization error of the statistical learning algorithm, up to a
martingale concentration term that is independent of the complexity of the
statistical learning method. This technique allows us to recover several
standard generalization bounds including a range of PAC-Bayesian and
information-theoretic guarantees, as well as generalizations thereof.
