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This research introduces a novel two-stage scoring function that disentangles core causal features from spurious correlations to improve model robustness without requiring group labels. By prioritizing informative samples based on this metric, the proposed algorithm achieves superior performance over existing debiasing techniques while reducing training data requirements to as little as 10%. The method offers a practical solution for mitigating bias in real-world datasets where spurious patterns often dominate the majority of training samples.
Mitigating Spurious Correlations with Memorization-Guided Dataset De-Biasing
arXiv:2606.02830v1 Announce Type: new Abstract: Real-world datasets often contain spurious correlations that are not causally related to the target label. When such correlations dominate the majority of training samples, models tend to rely on them, leading to misclassification of minority samples that do not exhibit the same spurious patterns. While a potential approach is to select subsets of data to better represent the minority samples, this may require access to group labels, which are typically unknown. Furthermore, as we demonstrate, widely used sample scoring functions in the invariant subset or coreset selection literature largely depend on spurious features and therefore fail to accurately capture the importance or difficulty of core, causally relevant features. Accordingly, we propose to mitigate spurious correlations by developing a two-stage sample scoring function that disentangles the learning dynamics of core and spurious features and evaluates their difficulty separately. Based on our proposed metric, we introduce a new algorithm to find and prioritize informative samples both with and without spurious correlations. Extensive experiments demonstrate that a standard ERM model trained on our selected samples achieves superior performance compared to state-of-the-art debiasing techniques, while requiring as little as 10\% of the original training data.
Abstract:Real-world datasets often contain spurious correlations that are not causally related to the target label. When such correlations dominate the majority of training samples, models tend to rely on them, leading to misclassification of minority samples that do not exhibit the same spurious patterns. While a potential approach is to select subsets of data to better represent the minority samples, this may require access to group labels, which are typically unknown. Furthermore, as we demonstrate, widely used sample scoring functions in the invariant subset or coreset selection literature largely depend on spurious features and therefore fail to accurately capture the importance or difficulty of core, causally relevant features. Accordingly, we propose to mitigate spurious correlations by developing a two-stage sample scoring function that disentangles the learning dynamics of core and spurious features and evaluates their difficulty separately. Based on our proposed metric, we introduce a new algorithm to find and prioritize informative samples both with and without spurious correlations. Extensive experiments demonstrate that a standard ERM model trained on our selected samples achieves superior performance compared to state-of-the-art debiasing techniques, while requiring as little as 10\% of the original training data.
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2606.02830 [cs.LG]
(or arXiv:2606.02830v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.02830 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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