BROT：基于深度重心回归的最优传输映射估计方法及其统计最优性

View PDF HTML (experimental)

Abstract:The optimal transport (OT) map provides a geometric transformation for aligning probability distributions and has become a useful tool in machine learning. However, existing estimators of the OT map still exhibit a gap between sharp statistical guarantees and practical parametric estimation based on stable training objectives. Theoretical estimators achieve minimax optimal convergence rates, but they are typically nonparametric and can incur demanding implementation design or inference costs. Practical estimators are parametric and scalable, but their statistical guarantees remain underexplored, and their min-max, adversarial-like training objectives can be sensitive to optimization algorithms. We propose BROT (Barycentric Regression for Optimal Transport), a simple two-step method that first computes the unregularized OT plan and then fits a deep neural network (DNN) to the induced barycentric targets by least-squares regression. Under standard regularity conditions, we prove that the DNN estimator of BROT attains the minimax convergence rate, when the ground-truth OT map is Lipschitz. Numerical studies on synthetic datasets and an image dataset show that BROT provides accurate map estimates, strong target distribution matching, and competitive transport costs, compared to existing estimation methods. Experiments on two downstream tasks, single-cell perturbation prediction and unsupervised domain adaptation, further suggest that the accurate estimation of BROT can translate into stronger task performance.

Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as:	arXiv:2609.06598 [cs.LG]
	(or arXiv:2609.06598v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2609.06598 arXiv-issued DOI via DataCite

Submission history

From: Kunwoong Kim [view email]
[v1] Sun, 6 Sep 2026 13:33:41 UTC (416 KB)
[v2] Thu, 5 Nov 2026 05:39:01 UTC (3,042 KB)