  This paper considers Importance Sampling (IS) for the estimation of tail
risks of a loss defined in terms of a sophisticated object such as a machine
learning feature map or a mixed integer linear optimisation formulation.
Assuming only black-box access to the loss and the distribution of the
underlying random vector, the paper presents an efficient IS algorithm for
estimating the Value at Risk and Conditional Value at Risk. The key challenge
in any IS procedure, namely, identifying an appropriate change-of-measure, is
automated with a self-structuring IS transformation that learns and replicates
the concentration properties of the conditional excess from less rare samples.
The resulting estimators enjoy asymptotically optimal variance reduction when
viewed in the logarithmic scale. Simulation experiments highlight the efficacy
and practicality of the proposed scheme
