Optimal Transport under Group Fairness Constraints
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915763367968768 |
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| author | Bleistein, Linus Dagréou, Mathieu Andrade, Francisco Boudou, Thomas Bellet, Aurélien |
| author_facet | Bleistein, Linus Dagréou, Mathieu Andrade, Francisco Boudou, Thomas Bellet, Aurélien |
| contents | Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT), we introduce a novel notion of group fairness requiring that the probability of matching two individuals from any two given groups in the OT plan satisfies a predefined target. We first propose a modified Sinkhorn algorithm to compute perfectly fair transport plans efficiently. Since exact fairness can significantly degrade matching quality in practice, we then develop two relaxation strategies. The first one involves solving a penalized OT problem, for which we derive novel finite-sample complexity guarantees. Our second strategy leverages bilevel optimization to learn a ground cost that induces a fair OT solution, and we establish a bound on the deviation of fairness when matching unseen data. Finally, we present empirical results illustrating the performance of our approaches and the trade-off between fairness and transport cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07144 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimal Transport under Group Fairness Constraints Bleistein, Linus Dagréou, Mathieu Andrade, Francisco Boudou, Thomas Bellet, Aurélien Machine Learning Statistics Theory Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT), we introduce a novel notion of group fairness requiring that the probability of matching two individuals from any two given groups in the OT plan satisfies a predefined target. We first propose a modified Sinkhorn algorithm to compute perfectly fair transport plans efficiently. Since exact fairness can significantly degrade matching quality in practice, we then develop two relaxation strategies. The first one involves solving a penalized OT problem, for which we derive novel finite-sample complexity guarantees. Our second strategy leverages bilevel optimization to learn a ground cost that induces a fair OT solution, and we establish a bound on the deviation of fairness when matching unseen data. Finally, we present empirical results illustrating the performance of our approaches and the trade-off between fairness and transport cost. |
| title | Optimal Transport under Group Fairness Constraints |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2601.07144 |