Probabilistic Stability Guarantees for Feature Attributions
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915433686237184 |
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| author | Jin, Helen Xue, Anton You, Weiqiu Goel, Surbhi Wong, Eric |
| author_facet | Jin, Helen Xue, Anton You, Weiqiu Goel, Surbhi Wong, Eric |
| contents | Stability guarantees have emerged as a principled way to evaluate feature attributions, but existing certification methods rely on heavily smoothed classifiers and often produce conservative guarantees. To address these limitations, we introduce soft stability and propose a simple, model-agnostic, sample-efficient stability certification algorithm (SCA) that yields non-trivial and interpretable guarantees for any attribution method. Moreover, we show that mild smoothing achieves a more favorable trade-off between accuracy and stability, avoiding the aggressive compromises made in prior certification methods. To explain this behavior, we use Boolean function analysis to derive a novel characterization of stability under smoothing. We evaluate SCA on vision and language tasks and demonstrate the effectiveness of soft stability in measuring the robustness of explanation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Stability Guarantees for Feature Attributions Jin, Helen Xue, Anton You, Weiqiu Goel, Surbhi Wong, Eric Machine Learning Artificial Intelligence Stability guarantees have emerged as a principled way to evaluate feature attributions, but existing certification methods rely on heavily smoothed classifiers and often produce conservative guarantees. To address these limitations, we introduce soft stability and propose a simple, model-agnostic, sample-efficient stability certification algorithm (SCA) that yields non-trivial and interpretable guarantees for any attribution method. Moreover, we show that mild smoothing achieves a more favorable trade-off between accuracy and stability, avoiding the aggressive compromises made in prior certification methods. To explain this behavior, we use Boolean function analysis to derive a novel characterization of stability under smoothing. We evaluate SCA on vision and language tasks and demonstrate the effectiveness of soft stability in measuring the robustness of explanation methods. |
| title | Probabilistic Stability Guarantees for Feature Attributions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13787 |