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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.16583 |
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| _version_ | 1866909900067569664 |
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| author | Sadiku, Shpresim Chitranshi, Kartikeya Kera, Hiroshi Pokutta, Sebastian |
| author_facet | Sadiku, Shpresim Chitranshi, Kartikeya Kera, Hiroshi Pokutta, Sebastian |
| contents | Plausible counterfactual explanations (p-CFEs) are perturbations that minimally modify inputs to change classifier decisions while remaining plausible under the data distribution. In this study, we demonstrate that classifiers can be trained on p-CFEs labeled with induced \emph{incorrect} target classes to classify unperturbed inputs with the original labels. While previous studies have shown that such learning is possible with adversarial perturbations, we extend this paradigm to p-CFEs. Interestingly, our experiments reveal that learning from p-CFEs is even more effective: the resulting classifiers achieve not only high in-distribution accuracy but also exhibit significantly reduced bias with respect to spurious correlations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Training on Plausible Counterfactuals Removes Spurious Correlations Sadiku, Shpresim Chitranshi, Kartikeya Kera, Hiroshi Pokutta, Sebastian Machine Learning Plausible counterfactual explanations (p-CFEs) are perturbations that minimally modify inputs to change classifier decisions while remaining plausible under the data distribution. In this study, we demonstrate that classifiers can be trained on p-CFEs labeled with induced \emph{incorrect} target classes to classify unperturbed inputs with the original labels. While previous studies have shown that such learning is possible with adversarial perturbations, we extend this paradigm to p-CFEs. Interestingly, our experiments reveal that learning from p-CFEs is even more effective: the resulting classifiers achieve not only high in-distribution accuracy but also exhibit significantly reduced bias with respect to spurious correlations. |
| title | Training on Plausible Counterfactuals Removes Spurious Correlations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.16583 |