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New method Light Entropic Optimal Transport developed for Riemannian manifolds

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Researchers have developed ManifoldLightOT, a novel method for learning entropic optimal transport (EOT) couplings directly on Riemannian manifolds. This approach utilizes geometry-specific Gibbs kernels and compatible potential parameterizations for various manifolds, including spheres, tori, SO(3), and SE(3). The parameters are optimized using Monte Carlo estimates, and the method has demonstrated superior performance compared to existing manifold OT techniques while enabling direct sampling. AI

IMPACT This research could improve generative modeling and domain adaptation techniques by providing more efficient and geometrically aware optimal transport solvers.

RANK_REASON This is a research paper detailing a new method for optimal transport on manifolds. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method Light Entropic Optimal Transport developed for Riemannian manifolds

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This is a research paper detailing a new method for optimal transport on manifolds. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xavier Aramayo-Carrasco, Petr Mokrov, Alexander Korotin ·

    Light Entropic Optimal Transport on Riemannian Manifolds

    arXiv:2610.03085v1 Announce Type: new Abstract: Entropic Optimal Transport (EOT) has become a practical framework for learning stochastic couplings between complex distributions, with applications in generative modeling and domain adaptation. However, most EOT solvers are designe…

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