  We present an extensive analysis of relative deviation bounds, including
detailed proofs of two-sided inequalities and their implications. We also give
detailed proofs of two-sided generalization bounds that hold in the general
case of unbounded loss functions, under the assumption that a moment of the
loss is bounded. These bounds are useful in the analysis of importance
weighting and other learning tasks such as unbounded regression.
