序贯概率分配的光滑分析
Smoothed Analysis of Sequential Probability Assignment
摘要：
本文首先研究了带上下文的序贯概率分配问题的平滑分析。我们研究信息理论上的最优极大极小率以及涉及最大似然估计甲骨文算法减少的框架。我们的方法建立了一个通用的减少，从最小化最大率的顺序概率分配平滑的对手，最小化最大率的转导学习。这导致了参数类和有限VC维类的最佳（对数）快速速率。在算法方面，我们开发了一种算法，有效地挖掘到MLE甲骨文，一般类的功能。我们表明，在一般条件下，这种算法的方法产生次线性遗憾。
英文摘要：
We initiate the study of smoothed analysis for the sequential probability assignment problem with contexts. We study information-theoretically optimal minmax rates as well as a framework for algorithmic reduction involving the maximum likelihood estimator oracle. Our approach establishes a general-purpose reduction from minimax rates for sequential probability assignment for smoothed adversaries to minimax rates for transductive learning. This leads to optimal (logarithmic) fast rates for parametric classes and classes with finite VC dimension. On the algorithmic front, we develop an algorithm that efficiently taps into the MLE oracle, for general classes of functions. We show that under general conditions this algorithmic approach yields sublinear regret.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
