In Orabona and Pál [2016], we introduced the shifted KT potentials, to remove the $\ln \ln T$ factor in the parameter-free learning with expert bound. In this short technical note, I show that this is equivalent to changing the prior in the Krichevsky--Trofimov algorithm. Then, I show how to use the same idea to remove the $\ln \ln T$ factor in the data-independent bound for the Squint algorithm.

翻译：在Orabona和Pál[2016]的工作中，我们引入了移位KT势函数，以去除无参数学习专家界限中的$\ln\ln T$因子。在这篇简短的技术笔记中，我证明了这等价于改变Krichevsky--Trofimov算法中的先验。随后，我展示了如何运用相同的思想来去除Squint算法中数据无关界限的$\ln\ln T$因子。