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Oct, 2018
动态遗憾的近端在线梯度是最优解
Proximal Online Gradient is Optimum for Dynamic Regret
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Yawei Zhao, Shuang Qiu, Ji Liu
TL;DR
本文研究了在线学习中基于动态后悔度的参考解决方案的变化以及静态后悔度参考解决方案的时间保持不变的差异，证明了基于在线梯度的近端算法是动态后悔度的最优算法。
Abstract
In
online learning
, the
dynamic regret metric
chooses the reference (optimal) solution that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the wh
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