Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Understand

Overparameterized neural networks can be highly accurate on average on an i.i.d.

• test set yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that hold on average but not in such groups).
• Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups.
• However, we find that naively applying group DRO to overparameterized neural networks fails: these models can perfectly fit the training data, and any model with vanishing average training loss also already has vanishing worst-case training loss.