arXiv stat.ML
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Wasserstein Contraction of Coordinate Ascent Variational Inference
arXiv:2605.30253v3 Announce Type: replace Abstract: We study the non-asymptotic contraction in Wasserstein distance of the sequential, parallel, and random-scan coordinate ascent variational inference algorithms. This is shown to hold under a functional smoothness condition of the optimality maps and a transportation-i