Randomized multi-class classification under system constraints: a unified approach via post-processing
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909965314162688 |
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| author | Chzhen, Evgenii Hebiri, Mohamed Taturyan, Gayane |
| author_facet | Chzhen, Evgenii Hebiri, Mohamed Taturyan, Gayane |
| contents | We study the problem of multi-class classification under system-level constraints expressible as linear functionals over randomized classifiers. We propose a post-processing approach that adjusts a given base classifier to satisfy general constraints without retraining. Our method formulates the problem as a linearly constrained stochastic program over randomized classifiers, and leverages entropic regularization and dual optimization techniques to construct a feasible solution. We provide finite-sample guarantees for the risk and constraint satisfaction for the final output of our algorithm under minimal assumptions. The framework accommodates a broad class of constraints, including fairness, abstention, and churn requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14246 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Randomized multi-class classification under system constraints: a unified approach via post-processing Chzhen, Evgenii Hebiri, Mohamed Taturyan, Gayane Optimization and Control Machine Learning We study the problem of multi-class classification under system-level constraints expressible as linear functionals over randomized classifiers. We propose a post-processing approach that adjusts a given base classifier to satisfy general constraints without retraining. Our method formulates the problem as a linearly constrained stochastic program over randomized classifiers, and leverages entropic regularization and dual optimization techniques to construct a feasible solution. We provide finite-sample guarantees for the risk and constraint satisfaction for the final output of our algorithm under minimal assumptions. The framework accommodates a broad class of constraints, including fairness, abstention, and churn requirements. |
| title | Randomized multi-class classification under system constraints: a unified approach via post-processing |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2512.14246 |