- The paper reveals that standard neural networks using ERM and group DRO can achieve near-zero training loss yet exhibit large worst-group generalization gaps.
- It shows that employing strong regularization, like enhanced weight decay or early stopping, improves worst-case accuracies by 10-40 percentage points.
- The study introduces scalable stochastic optimization and group adjustment techniques to further boost robustness across diverse tasks.
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Sagawa et al. have contributed an important investigation into the behavior of overparameterized neural networks under distributionally robust optimization (DRO). The paper specifically focuses on the generalization issues that arise when models are trained to minimize worst-case loss over pre-defined groups—a scenario not adequately handled by empirical risk minimization (ERM).
Problem Definition and Context
The authors address a significant limitation in current machine learning practices: models trained with ERM often fail on rare or atypical groups within the data distribution. This inadequacy poses risks when such models rely on spurious correlations, leading to poor performance on specific subpopulations, which could contradict fairness and robustness requirements.
By employing a DRO framework, the aim shifts from minimizing average training loss to minimizing the worst-case loss over specified groups. However, the authors identify that naïve application of group DRO to overparameterized neural networks fails to yield significant improvements in worst-case generalization because both ERM and group DRO models can achieve near-perfect training accuracies, yet exhibit large generalization gaps for the worst-case groups.
Methodology and Key Findings
Experiments and Results
The investigation spans several tasks: natural language inference (MultiNLI dataset), facial attribute recognition (CelebA dataset), and bird photograph recognition (Waterbirds dataset). The findings indicate that standard overparameterized models trained to minimize either ERM or group DRO objectives achieve almost zero training loss, but maintain substantial worst-group accuracy gaps at test time. This occurs despite high average test accuracies, highlighting that worst-group generalization is more challenging and insufficiently addressed by standard training protocols.
Role of Regularization
A critical discovery is the role of stringent regularization methods in reducing worst-group generalization gaps. By employing stronger than typical $$ penalties or early stopping, the authors demonstrate substantial improvements in worst-case accuracies—showing gains of 10-40 percentage points across tasks. This result underlines that regularization, which need not be critical for average performance, is essential for improving worst-case performance.
Particularly, they show that when models are unable to perfectly fit the training data (owing to strong regularization), group DRO can outperform ERM significantly. This suggests that worst-group performance in neural networks is heavily dependent on achieving balanced generalization across all groups rather than just the average case.
Group Adjustments
The authors further enhance group DRO through group adjustments, which prioritize lower training loss on groups with larger generalization gaps. This adjustment leverages structural risk minimization principles to provide improved worst-group accuracy. Empirically, this method shows additional gains, particularly reducing error rates by compelling degrees for Waterbirds dataset.
Comparison with Importance Weighting
The work also evaluates importance weighting as a baseline for robust learning under group shifts. Although importance weighting can improve robust accuracy, especially on balanced test distributions, it does not always achieve uniform improvement across all tasks. The theoretical underpinning indicates that, unlike convex settings where equivalence to DRO can be established, in non-convex settings (like neural networks), DRO has inherent advantages by directly focusing on the worst-case distribution. This empirical and theoretical juxtaposition of DRO vs. importance weighting strengthens the argument for DRO in handling distributional robustness.
Algorithm and Scalability
To efficiently train group DRO models, the authors introduce a scalable stochastic optimization algorithm with convergence guarantees. This novel approach ensures stable, efficient training even for large-scale models and datasets, providing a practical framework for implementing distributionally robust neural networks.
Implications and Future Directions
This paper's contributions have important implications for both theoretical and practical applications. Theoretically, it advances the discussion on generalization in neural networks by showing that perfect training accuracy does not guarantee robust generalization across all groups. Practically, this informs better training methodologies for applications requiring equitable model performance across diverse groups, such as fairness in AI, medical diagnostics, and more.
Future research could explore further the intersection of regularization techniques and DRO under various data shifts, considering different types of spurious correlations and extending to other domains beyond NLP and computer vision.
Conclusion
Sagawa et al. deliver compelling evidence that regularization plays a crucial role in ensuring worst-case generalization for overparameterized neural networks under a DRO framework. By proposing robust models that prioritize consistency across groups, they open avenues for more equitable and reliable AI systems. The paper offers a robust methodological framework and an efficient algorithm, setting a significant precedent for future research in distributionally robust machine learning.