Confidence-Based Model Selection: When to Take Shortcuts for
  Subpopulation Shifts
Annie S. Chen Yoonho Lee Amrith Setlur Sergey Levine Chelsea Finn
Effective machine learning models learn both robust features that directly
determine the outcome of interest (e.g., an object with wheels is more likely
to be a car), and shortcut features (e.g., an object on a road is more likely
to be a car). The latter can be a source of error under distributional shift,
when the correlations change at test-time. The prevailing sentiment in the
robustness literature is to avoid such correlative shortcut features and learn
robust predictors. However, while robust predictors perform better on
worst-case distributional shifts, they often sacrifice accuracy on majority
subpopulations. In this paper, we argue that shortcut features should not be
entirely discarded. Instead, if we can identify the subpopulation to which an
input belongs, we can adaptively choose among models with different strengths
to achieve high performance on both majority and minority subpopulations. We
propose COnfidence-baSed MOdel Selection (CosMoS), where we observe that model
confidence can effectively guide model selection. Notably, CosMoS does not
require any target labels or group annotations, either of which may be
difficult to obtain or unavailable. We evaluate CosMoS on four datasets with
spurious correlations, each with multiple test sets with varying levels of data
distribution shift. We find that CosMoS achieves 2-5% lower average regret
across all subpopulations, compared to using only robust predictors or other
model aggregation methods.