  In this work, we consider the setting of learning problems under a wide class
of spectral risk (or "L-risk") functions, where a Lipschitz-continuous spectral
density is used to flexibly assign weight to extreme loss values. We obtain
excess risk guarantees for a derivative-free learning procedure under unbounded
heavy-tailed loss distributions, and propose a computationally efficient
implementation which empirically outperforms traditional risk minimizers in
terms of balancing spectral risk and misclassification error.
