  The generalization ability of machine learning models degrades significantly
when the test distribution shifts away from the training distribution. We
investigate the problem of training models that are robust to shifts caused by
changes in the distribution of class-priors or group-priors. The presence of
skewed training priors can often lead to the models overfitting to spurious
features. Unlike existing methods, which optimize for either the worst or the
average performance over classes or groups, our work is motivated by the need
for finer control over the robustness properties of the model. We present an
extremely lightweight post-hoc approach that performs scaling adjustments to
predictions from a pre-trained model, with the goal of minimizing a
distributionally robust loss around a chosen target distribution. These
adjustments are computed by solving a constrained optimization problem on a
validation set and applied to the model during test time. Our constrained
optimization objective is inspired by a natural notion of robustness to
controlled distribution shifts. Our method comes with provable guarantees and
empirically makes a strong case for distributional robust post-hoc classifiers.
An empirical implementation is available at
https://github.com/weijiaheng/Drops.

Machine Learning;Artificial Intelligence