Overparameterized neural networks trained to minimize average loss 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) provides an approach for learning models that instead minimize worst-case training loss over a set of pre-defined groups. 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 will also already have vanishing worst-case training loss. Instead, their poor worst-case performance arises from poor generalization on some groups. By coupling group DRO models with increased regularization-stronger-than-typical 2 regularization or early stopping-we achieve substantially higher worst-group accuracies, with 10-40 percentage point improvements over standard models on a natural language inference task and two image tasks, while maintaining high average accuracies. Our results suggest that regularization is critical for worst-group generalization in the overparameterized regime, even if it is not needed for average generalization. Finally, we introduce and provide convergence guarantees for a stochastic optimizer for this group DRO setting, underpinning the empirical study above.
This paper describes a method of training neural networks to be robust to a worse case mixture of a set of predefined example attributes. This is done with a loss in accuracy in the average case but improvements in the worse case. The proposed algorithm is relatively simple and convergence rates are also given for this new algorithm.
Building neural networks that perform well in the face of group-level worse case test-set distributions is a very important problem particularly in areas such as health and safety-critical applications as past work points out. This paper shows good results in the worse case and additionally shows that the common technique of importance reweighting cannot arrive at the same solution. The convergence analyses also yield additional insight into this new algorithm. The paper is well written and relatively easy to understand with good details on the experimental setup. The algorithm has a downside in that the groups must be known a priori, is it possible for these groups to be learnt? Also, can using a hinge loss also improve robustness to the worse case examples?
However, there are some unanswered questions in the paper. What is the effect on the training time of this algorithm? Is it just the time for an additional forward prop? What is the effect on the worse-case examples of weight decay on the Bert model? Even though it hurts the average performance does it improve the worse case at all? In the last line of algorithm 1 why is q_G used instead of q_g?
Other comments:
At the bottom of P5: The ordering of 93.4% and 97.1% seem to be reversed.
Above eq. 5 , \delta_g seems to be overloaded. In the paragraph, it first refers to the generalization gap and then later to a heuristic.
Table 2: The drop in average accuracy for waterbirds does not seem 'small'.
Bottom of P8: 'background is more unique', it seems this is supposed to mean the background appears less often?
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Update after rebuttal:
Thanks for the detailed answers to my comments and the additional experiments done with a hinge loss. I will keep my rating.