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
this https URL

训练模型以适应由类先验或群组先验的分布变化引起的偏移问题并不容易，我们提出一种极其轻量级的事后方法，通过在验证集上求解约束优化问题并应用于模型，从而在测试时尽量减小选定目标分布周围的分布鲁棒性损失，并带有可证明的保证和实证的结果证明，表明我们的方法非常适用于分布鲁棒的事后分类器。