Extends minimum-distance estimators to Hellinger distance via reverse data processing inequalities, yielding the first near-linear time algorithms for univariate mixtures of log-concave densities and Gaussians with near-optimal sample complexity.
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Provides asymptotic distributions for entropic OT plans and potentials under vanishing regularization and links self-transport barycentric projections to score functions.
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The entropic optimal (self-)transport problem: Limit distributions for decreasing regularization with application to score function estimation
Provides asymptotic distributions for entropic OT plans and potentials under vanishing regularization and links self-transport barycentric projections to score functions.