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New research details convergence rates for sliced optimal transport

By PulseAugur Editorial · [1 sources] ·

A new paper on arXiv details convergence rates for distribution matching using sliced optimal transport. The research establishes quantitative non-asymptotic rates by deriving Lojasiewicz-type inequalities for the Sliced-Wasserstein objective, particularly for Gaussian distributions. The study also includes numerical experiments to illustrate the theoretical predictions regarding dimension, step-size, and the benefits of orthonormal-basis sampling. AI

IMPACT This research contributes to the theoretical understanding of distribution matching techniques, potentially improving the efficiency of certain machine learning algorithms.

RANK_REASON The cluster contains a single arXiv paper detailing theoretical research in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New research details convergence rates for sliced optimal transport

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The cluster contains a single arXiv paper detailing theoretical research in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Gauthier Thurin (ENS-PSL), Claire Boyer (LMO, IUF, CELESTE), Kimia Nadjahi (ENS-PSL) ·

    Convergence Rates for Distribution Matching with Sliced Optimal Transport

    arXiv:2602.10691v2 Announce Type: replace Abstract: We study the slice-matching scheme, an efficient iterative method for distribution matching based on sliced optimal transport. We investigate convergence to the target distribution and derive quantitative non-asymptotic rates. T…

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