A grid-sketching technique enables ε-accurate estimation of W₂² between α-Hölder smooth distributions on (0,1)^d in time ε^{-max(2, (d+1+o(1))/(1+α))}.
Information and Inference: A Journal of the IMA , volume=
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Entropic optimal transport yields a clustering loss with the same global optimum as log-likelihood but a better-behaved optimization surface, outperforming standard EM in experiments.
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Optimizing Computational-Statistical Runtime for Wasserstein Distance Estimation
A grid-sketching technique enables ε-accurate estimation of W₂² between α-Hölder smooth distributions on (0,1)^d in time ε^{-max(2, (d+1+o(1))/(1+α))}.
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On Model-Based Clustering With Entropic Optimal Transport
Entropic optimal transport yields a clustering loss with the same global optimum as log-likelihood but a better-behaved optimization surface, outperforming standard EM in experiments.
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