Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport
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arXiv
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| Format: | Preprint |
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2026
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| author | Naumann, Philip Kauffmann, Jacob Müller, Klaus-Robert Montavon, Grégoire |
| author_facet | Naumann, Philip Kauffmann, Jacob Müller, Klaus-Robert Montavon, Grégoire |
| contents | Optimal transport (OT) is a central framework for modeling distribution shifts. Because OT compares distributions directly in input space, a well-designed ground metric between observations is essential to ensure that the optimizer does not violate the true geometry of change. We propose Displacement-Reshaped Optimal Transport (ReshapeOT), a method that reshapes the ground metric by integrating observed sample displacements as an additional source of knowledge. Technically, ReshapeOT replaces the Euclidean metric with a Mahalanobis distance estimated from displacement second moments. This effectively carves expressways through the input space, inviting transport solutions that better align with observed displacements. Our method is computationally lightweight, integrates seamlessly into any OT solver that operates on a cost matrix, and can be kernelized for further flexibility. Experiments on synthetic and real-world data show that ReshapeOT achieves substantial gains in transport reliability. We further demonstrate our method's usefulness in two practical use cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04965 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport Naumann, Philip Kauffmann, Jacob Müller, Klaus-Robert Montavon, Grégoire Machine Learning Artificial Intelligence Optimal transport (OT) is a central framework for modeling distribution shifts. Because OT compares distributions directly in input space, a well-designed ground metric between observations is essential to ensure that the optimizer does not violate the true geometry of change. We propose Displacement-Reshaped Optimal Transport (ReshapeOT), a method that reshapes the ground metric by integrating observed sample displacements as an additional source of knowledge. Technically, ReshapeOT replaces the Euclidean metric with a Mahalanobis distance estimated from displacement second moments. This effectively carves expressways through the input space, inviting transport solutions that better align with observed displacements. Our method is computationally lightweight, integrates seamlessly into any OT solver that operates on a cost matrix, and can be kernelized for further flexibility. Experiments on synthetic and real-world data show that ReshapeOT achieves substantial gains in transport reliability. We further demonstrate our method's usefulness in two practical use cases. |
| title | Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.04965 |