Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
This addresses robustness issues for safe deployment in real-world settings, though it appears incremental as it builds on existing approaches like Bayesian inference.
The paper tackles the problem of poor machine learning model performance under subpopulation shifts by developing group-aware prior (GAP) distributions over neural network parameters, resulting in state-of-the-art performance even with minimal retraining.
Machine learning models often perform poorly under subpopulation shifts in the data distribution. Developing methods that allow machine learning models to better generalize to such shifts is crucial for safe deployment in real-world settings. In this paper, we develop a family of group-aware prior (GAP) distributions over neural network parameters that explicitly favor models that generalize well under subpopulation shifts. We design a simple group-aware prior that only requires access to a small set of data with group information and demonstrate that training with this prior yields state-of-the-art performance -- even when only retraining the final layer of a previously trained non-robust model. Group aware-priors are conceptually simple, complementary to existing approaches, such as attribute pseudo labeling and data reweighting, and open up promising new avenues for harnessing Bayesian inference to enable robustness to subpopulation shifts.