论文标题
一种通用的在线算法，用于翻译和规模不变预测，并提供专家建议
A Generalized Online Algorithm for Translation and Scale Invariant Prediction with Expert Advice
论文作者
论文摘要
在这项工作中，我们旨在通过专家建议创建一个完全在线算法的预测框架，该框架无需翻译和无限制专家损失。我们的目标是创建适合在各种应用中使用的广义算法。为此，我们研究了通过专家建议问题进行顺序预测中的通用竞争类别的预期遗憾，预期的遗憾衡量了我们预测算法的损失与竞争中“最佳”专家选择策略的损失之间的差异。我们使用通用预测的观点设计算法，以与指定的专家选择策略竞争，这不一定是固定的专家选择。我们想要与之竞争的专家选择策略类别完全取决于当前的特定应用程序，并且是通用的，这使得我们的广义算法适合在许多不同的问题中使用。我们表明，我们的算法及其性能范围不需要关于损失顺序的初步知识，而其性能界限是二阶，以平方损耗的总和表示。我们的遗憾界限是在任意尺度和损失的翻译下稳定的。
In this work, we aim to create a completely online algorithmic framework for prediction with expert advice that is translation-free and scale-free of the expert losses. Our goal is to create a generalized algorithm that is suitable for use in a wide variety of applications. For this purpose, we study the expected regret of our algorithm against a generic competition class in the sequential prediction by expert advice problem, where the expected regret measures the difference between the losses of our prediction algorithm and the losses of the 'best' expert selection strategy in the competition. We design our algorithm using the universal prediction perspective to compete against a specified class of expert selection strategies, which is not necessarily a fixed expert selection. The class of expert selection strategies that we want to compete against is purely determined by the specific application at hand and is left generic, which makes our generalized algorithm suitable for use in many different problems. We show that no preliminary knowledge about the loss sequence is required by our algorithm and its performance bounds, which are second order, expressed in terms of sums of squared losses. Our regret bounds are stable under arbitrary scalings and translations of the losses.
