mlsanityData to 14 Oct 2026

Breakdown properties of optimal transport maps: general transportation costs

arXiv 2603.16005 ↗First posted 16 Mar 2026math.ST stat.ML stat.THcited by 1

Not on the map. Why?

Abstract (arXiv)

Two recent works, Avella-Medina and Gonz'alez-Sanz (2026) and Passeggeri and Paindaveine (2026), studied the robustness of the optimal transport map through its breakdown point, i.e., the smallest fraction of contamination that can make the map take arbitrarily aberrant values. Their main finding is the following: let P and Q denote the target and reference measures, respectively, and let T be the optimal transport map for the squared Euclidean cost. Then, the breakdown point of T(u), when P is perturbed and Q is fixed, coincides with the Tukey depth of u relative to Q. In this note, we extend this result to general convex cost functions, demonstrating that the cost function does not have any impact on the breakdown point of the optimal transport map. Our contribution provides a definitive characterization of the breakdown point of the optimal transport map. In particular, it shows that for a broad class of regular cost functions, all transport-based quantiles enjoy the same high breakdown point properties.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 22 references that are in the map. First posted on arXiv 16 Mar 2026.

Builds on

3 of 22 references.

References · 3

  1. 2021Tukey's Depth for Object Datacited by 4
  2. 2024On the breakdown point of transport-based quantilescited by 2
  3. 2024On the robustness of semi-discrete optimal transportcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.