  Knowledge distillation has proven to be an effective technique in improving
the performance a student model using predictions from a teacher model.
However, recent work has shown that gains in average efficiency are not uniform
across subgroups in the data, and in particular can often come at the cost of
accuracy on rare subgroups and classes. To preserve strong performance across
classes that may follow a long-tailed distribution, we develop distillation
techniques that are tailored to improve the student's worst-class performance.
Specifically, we introduce robust optimization objectives in different
combinations for the teacher and student, and further allow for training with
any tradeoff between the overall accuracy and the robust worst-class objective.
We show empirically that our robust distillation techniques not only achieve
better worst-class performance, but also lead to Pareto improvement in the
tradeoff between overall performance and worst-class performance compared to
other baseline methods. Theoretically, we provide insights into what makes a
good teacher when the goal is to train a robust student.
