  Many modern machine learning applications come with complex and nuanced
design goals such as minimizing the worst-case error, satisfying a given
precision or recall target, or enforcing group-fairness constraints. Popular
techniques for optimizing such non-decomposable objectives reduce the problem
into a sequence of cost-sensitive learning tasks, each of which is then solved
by re-weighting the training loss with example-specific costs. We point out
that the standard approach of re-weighting the loss to incorporate label costs
can produce unsatisfactory results when used to train over-parameterized
models. As a remedy, we propose new cost-sensitive losses that extend the
classical idea of logit adjustment to handle more general cost matrices. Our
losses are calibrated, and can be further improved with distilled labels from a
teacher model. Through experiments on benchmark image datasets, we showcase the
effectiveness of our approach in training ResNet models with common robust and
constrained optimization objectives.
