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Langevin Diffusion Approximation to Same Marginal Schr\"{o}dinger Bridge

Probability 2026-04-06 v2 Machine Learning

Abstract

We introduce a novel approximation to the same marginal Schr\"{o}dinger bridge using the Langevin diffusion. As ε0\varepsilon \downarrow 0, it is known that the barycentric projection (also known as the entropic Brenier map) of the Schr\"{o}dinger bridge converges to the Brenier map, which is the identity. Our diffusion approximation is leveraged to show that, under suitable assumptions, the difference between the two is ε\varepsilon times the gradient of the marginal log density (i.e., the score function), in L2\mathbf{L}^2. More generally, we show that the family of Markov operators, indexed by ε>0\varepsilon > 0, derived from integrating test functions against the conditional density of the static Schr\"{o}dinger bridge at temperature ε\varepsilon, admits a derivative at ε=0\varepsilon=0 given by the generator of the Langevin semigroup. Hence, these operators satisfy an approximate semigroup property at low temperatures.

Cite

@article{arxiv.2505.07647,
  title  = {Langevin Diffusion Approximation to Same Marginal Schr\"{o}dinger Bridge},
  author = {Medha Agarwal and Zaid Harchaoui and Garrett Mulcahy and Soumik Pal},
  journal= {arXiv preprint arXiv:2505.07647},
  year   = {2026}
}

Comments

Final version. arXiv admin note: substantial text overlap with arXiv:2406.10823

R2 v1 2026-06-28T23:29:44.193Z