Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations (2107.00520v5)

Abstract: In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For example, in classifying animals in natural images, the background, which is a nuisance, can predict the type of animal. This nuisance-label relationship does not always hold, and the performance of a model trained under one such relationship may be poor on data with a different nuisance-label relationship. To build predictive models that perform well regardless of the nuisance-label relationship, we develop Nuisance-Randomized Distillation (NURD). We introduce the nuisance-randomized distribution, a distribution where the nuisance and the label are independent. Under this distribution, we define the set of representations such that conditioning on any member, the nuisance and the label remain independent. We prove that the representations in this set always perform better than chance, while representations outside of this set may not. NURD finds a representation from this set that is most informative of the label under the nuisance-randomized distribution, and we prove that this representation achieves the highest performance regardless of the nuisance-label relationship. We evaluate NURD on several tasks including chest X-ray classification where, using non-lung patches as the nuisance, NURD produces models that predict pneumonia under strong spurious correlations.

• The paper proposes a novel nuisance-randomized distribution that decouples spurious correlations between labels and nuisance variables.
• It defines uncorrelating representations to mathematically guarantee consistent performance, even under shifting data conditions.
• The NuRD algorithm demonstrates competitive accuracy on tasks like Colored-MNIST and Waterbirds, affirming its practical robustness.

Overview of "Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations"

The paper, titled "Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations," addresses the challenge of ensuring robust model performance when spurious correlations are present between the label and a nuisance variable. This is a common issue in machine learning, where predictive models might inadvertently exploit spurious relationships, leading to significant drops in performance when facing out-of-distribution (OOD) data with different nuisance-label correlations.

Key Contributions

1. Nuisance-randomized Distribution: The authors propose a methodology to mitigate reliance on spurious correlations by introducing the nuisance-randomized distribution. This distribution is constructed so that the nuisance and the label become independent, detaching the spurious ties that a model might otherwise exploit.
2. Uncorrelating Representations: They define the notion of uncorrelating representations that preserve the conditional independence of the label and the nuisance, even when conditioned on these representations. Through this framework, the authors mathematically guarantee that models using such representations will perform at least as well as random guessing in any distribution from the nuisance-varying family.
3. NuRD Algorithm: The paper introduces NuRD (Nuisance-Randomized Distillation), an algorithm that seeks to find the most informative representation of the label under the nuisance-randomized distribution. It does so by optimizing an objective function that maximizes the mutual information between the label and the uncorrelating representation while penalizing any conditional dependence with the nuisance.

Numerical Results

The paper presents results on several tasks including:

• Class-conditional Gaussian data: NuRD achieves approximately 58% test accuracy, close to the optimal representation's performance.
• Colored-MNIST: The algorithm matches the oracle accuracy of 75%, outperforming basic ERM techniques significantly.
• Waterbirds: With a high-dimensional nuisance (the background of bird images), NuRD performs exceptionally well, achieving up to 83% accuracy by breaking spurious correlations linked with the background.
• Chest X-rays for pneumonia detection: NuRD is able to generalize better, especially when strong spurious correlations with hospital protocols are present, thus outperforming traditional models reliant on nuisance-prone areas.

Implications and Future Development

The methodology and results discussed in this paper have pertinent implications for the development of robust machine learning models across various domains. This approach could substantially enhance model generalization in areas where labeled data availability varies widely, and nuisance factors are influential, such as in healthcare or autonomous vehicle datasets.

The authors suggest future work could focus on better synergizing the two steps within the NuRD framework or exploring the use of additional priors in the nuisance-randomization process. Furthermore, as nuanced distributions are used to simulate varying nuisance-label relationships, understanding their practical implementations and limitations in real datasets could provide additional insights into the robustness of the algorithm.

The paper provides a comprehensive and theoretical foundation for addressing nuisance-induced spurious correlations, paving a pathway to more resilient and reliable machine learning models that maintain efficacy across diverse and shifting data landscapes.