Machine-learned predictors, although achieving very good results for inputs resembling training data, cannot possibly provide perfect predictions in all situations. Still, decision-making systems that are based on such predictors need not only to benefit from good predictions but also to achieve a decent performance when the predictions are inadequate. In this paper, we propose a prediction setup for arbitrary metrical task systems (MTS) (e.g., caching, k-server and convex body chasing) and online matching on the line. We utilize results from the theory of online algorithms to show how to make the setup robust. Specifically for caching, we present an algorithm whose performance, as a function of the prediction error, is exponentially better than what is achievable for general MTS. Finally, we present an empirical evaluation of our methods on real world datasets, which suggests practicality.
翻译:---
机器学习预测器可以在类似训练数据的输入上获得非常好的结果,但不可能在所有情况下提供完美的预测。尽管如此,基于这种预测器的决策系统不仅需要受益于良好的预测,而且需要在预测不足时实现良好的性能。在本文中,我们提出了一种任意测量任务系统(MTS)(例如caching、k-server和凸体追踪)和在线匹配的预测设置。我们利用在线算法理论的结果来展示如何使该设置具有鲁棒性。对于caching,我们提出了一种算法,其表现在预测误差的函数下,比一般MTS可实现的性能指数更高。最后,我们在现实世界的数据集上对我们的方法进行了实证评估,这表明了其实用性。