使用减少数量预测的缓存和MTS算法
Algorithms for Caching and MTS with reduced number of predictions
AI总结：
本文为缓存和一般MTS设计节俭使用动作预测的算法，在预测数量受限时刻画一致性与平滑性，并在无限制时恢复已有最优保证。
摘要：
机器学习增强算法利用预测来获得超越其最坏情况界限的性能。产生这些预测可能是一项代价高昂的操作——这促使Im等人在2022年引入了对节俭使用预测的算法的研究。我们针对Antoniadis等人在2020年提出的带有动作预测的缓存和MTS设计了节俭算法，重点关注一致性（在完美预测下的性能）和平滑性（其性能对预测误差的依赖性）参数。我们的缓存算法是1-一致的、稳健的，并且其平滑性随着可用预测数量的减少而恶化。我们提出了一种用于一般MTS的算法，其一致性和平滑性都随着预测数量的减少而线性变化。在没有可用预测数量限制的情况下，两种算法都匹配了Antoniadis等人在2020年取得的早期保证。
英文摘要：
ML-augmented algorithms utilize predictions to achieve performance beyond their worst-case bounds. Producing these predictions might be a costly operation -- this motivated Im et al. '22 to introduce the study of algorithms which use predictions parsimoniously. We design parsimonious algorithms for caching and MTS with action predictions, proposed by Antoniadis et al. '20, focusing on the parameters of consistency (performance with perfect predictions) and smoothness (dependence of their performance on the prediction error). Our algorithm for caching is 1-consistent, robust, and its smoothness deteriorates with the decreasing number of available predictions. We propose an algorithm for general MTS whose consistency and smoothness both scale linearly with the decreasing number of predictions. Without the restriction on the number of available predictions, both algorithms match the earlier guarantees achieved by Antoniadis et al. '20.
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。
