Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features

Published: 21 Sept 2023, Last Modified: 02 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: invariant prediction, spurious correlations, out-of-distribution generalization, domain generalization, domain adaptation, test-time domain adaptation
TL;DR: We show that spurious features can be *safely* harnessed in the test domain *without labels*, using only invariant-feature pseudo-labels, and propose an algorithm for doing so.
Abstract: To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label changes across domains. However, unstable features often carry complementary information that could boost performance if used correctly in the test domain. In this work, we show how this can be done without test-domain labels. In particular, we prove that pseudo-labels based on stable features provide sufficient guidance for doing so, provided that stable and unstable features are conditionally independent given the label. Based on this theoretical insight, we propose Stable Feature Boosting (SFB), an algorithm for: (i) learning a predictor that separates stable and conditionally-independent unstable features; and (ii) using the stable-feature predictions to adapt the unstable-feature predictions in the test domain. Theoretically, we prove that SFB can learn an asymptotically-optimal predictor without test-domain labels. Empirically, we demonstrate the effectiveness of SFB on real and synthetic data.
Supplementary Material: pdf
Submission Number: 14527