This paper discusses the problem of adaptive estimation of a univariate
object like the value of a regression function at a given point or a linear
functional in a linear inverse problem. We consider an adaptive procedure
originated from Lepski [Theory Probab. Appl. 35 (1990) 454--466.
文章探讨带有噪声梯度反馈的非平稳随机优化框架,在比较序列变化的动态策略中,研究在线学习算法的动态后悔,并引入了 Total Variation ball 等新颖变分约束来建模比较序列,并基于基于小波的非参数回归理论,设计出一个多项式时间算法,并证明了该算法达到了几乎最优的动态后悔,该策略适应未知的半径。