  In risk-sensitive learning, one aims to find a hypothesis that minimizes a
risk-averse (or risk-seeking) measure of loss, instead of the standard expected
loss. In this paper, we propose to study the generalization properties of
risk-sensitive learning schemes whose optimand is described via optimized
certainty equivalents (OCE): our general scheme can handle various known risks,
e.g., the entropic risk, mean-variance, and conditional value-at-risk, as
special cases. We provide two learning bounds on the performance of empirical
OCE minimizer. The first result gives an OCE guarantee based on the Rademacher
average of the hypothesis space, which generalizes and improves existing
results on the expected loss and the conditional value-at-risk. The second
result, based on a novel variance-based characterization of OCE, gives an
expected loss guarantee with a suppressed dependence on the smoothness of the
selected OCE. Finally, we demonstrate the practical implications of the
proposed bounds via exploratory experiments on neural networks.
