  We consider regression with square loss and general classes of functions
without the boundedness assumption. We introduce a notion of offset Rademacher
complexity that provides a transparent way to study localization both in
expectation and in high probability. For any (possibly non-convex) class, the
excess loss of a two-step estimator is shown to be upper bounded by this offset
complexity through a novel geometric inequality. In the convex case, the
estimator reduces to an empirical risk minimizer. The method recovers the
results of \citep{RakSriTsy15} for the bounded case while also providing
guarantees without the boundedness assumption.
