论文标题
Renyi熵的实际估计
Practical Estimation of Renyi Entropy
论文作者
论文摘要
熵估计是密码学，统计，机器学习中许多应用的重要问题。尽管已经开发出有关样品复杂性的最佳估计量，但在本文中我们仍然存在一些挑战。贡献是一种新型的估计器，直接基于生日悖论。事实证明，这是相当简单的，并提供了与显式cons剂的较高信心界限。我们还讨论如何使用流算法来大规模改善记忆力消费。最后但并非最不重要的一点是，我们研究了低或中等制度中的估计问题，从而调整了估计器并证明了严密的界限。
Entropy Estimation is an important problem with many applications in cryptography, statistic,machine learning. Although the estimators optimal with respect to the sample complexity have beenrecently developed, there are still some challenges we address in this paper.The contribution is a novel estimator which is built directly on the birthday paradox. Theanalysis turns out to be considerably simpler and offer superior confidence bounds with explicitconstants. We also discuss how streaming algorithm can be used to massively improve memoryconsumption. Last but not least, we study the problem of estimation in low or moderate regimes,adapting the estimator and proving rigorus bounds.
