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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15867 |
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| _version_ | 1866914498684649472 |
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| author | Monteiro, Adriana Laurindo Loubes, Jean-Michel |
| author_facet | Monteiro, Adriana Laurindo Loubes, Jean-Michel |
| contents | The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainability framework that leverages Optimal Transport and Distributionally Robust Optimization to analyze how ML algorithms respond to constrained data perturbations. Our approach enforces constraints on feature-level statistics (e.g., brightness, age distribution), generating realistic perturbations that preserve semantic structure. We provide a model-agnostic diagnostic bench that applies to both tabular and image domains with solid theoretical guarantees. We validate the approach on real-world datasets providing interpretable robustness diagnostics that complement standard evaluation and fairness auditing tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15867 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations Monteiro, Adriana Laurindo Loubes, Jean-Michel Machine Learning The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainability framework that leverages Optimal Transport and Distributionally Robust Optimization to analyze how ML algorithms respond to constrained data perturbations. Our approach enforces constraints on feature-level statistics (e.g., brightness, age distribution), generating realistic perturbations that preserve semantic structure. We provide a model-agnostic diagnostic bench that applies to both tabular and image domains with solid theoretical guarantees. We validate the approach on real-world datasets providing interpretable robustness diagnostics that complement standard evaluation and fairness auditing tools. |
| title | Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.15867 |