论文标题
熵运输成本的提高了中央限制定理和快速收敛率
An improved central limit theorem and fast convergence rates for entropic transportation costs
论文作者
论文摘要
我们证明，以人口成本为中心的subgaussian概率措施之间的熵运输成本是一个中心限制定理。这是第一个结果，它允许对不一定是离散的措施之间的熵最佳传输有效地推断。在紧凑的案例中，我们通过经验措施之间的预期熵运输成本来补充这些结果。我们的证明是基于增强熵最佳运输问题的双重解决方案的收敛结果。
We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost. This is the first result which allows for asymptotically valid inference for entropic optimal transport between measures which are not necessarily discrete. In the compactly supported case, we complement these results with new, faster, convergence rates for the expected entropic transportation cost between empirical measures. Our proof is based on strengthening convergence results for dual solutions to the entropic optimal transport problem.
