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：机器学习的预测模型尽管在处理与训练数据相似的输入时表现出色，但无法在所有场景下提供完美预测。然而，基于此类预测的决策系统不仅需要从优质预测中获益，还需在预测效果不佳时维持合理性能。本文针对一般度量任务系统（如缓存、k-服务器及凸体追踪）和直线上的在线匹配问题，提出了一种预测框架。我们利用在线算法理论的研究成果，展示了如何增强该框架的鲁棒性。特别地，针对缓存问题，我们设计了一种算法，其性能随预测误差的变化呈指数级优于一般度量任务系统的可行方案。最后，通过在真实数据集上的实证评估，验证了所提方法的实用性。