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.

翻译：机器学习增强型算法利用预测实现超越最坏情况边界的性能。生成这些预测可能成本高昂——这促使Im等人'22开创性地引入对节俭使用预测的算法研究。我们针对Antoniadis等人'20提出的带动作预测的缓存与MTS问题，设计了节俭型算法，重点分析一致性（完美预测下的性能）和平滑性（性能对预测误差的依赖程度）参数。我们的缓存算法具有1-一致性和鲁棒性，其平滑性随可用预测数量减少而恶化。我们提出通用MTS算法，其一致性和平滑性均随预测数量减少呈线性变化。在无可用预测数量限制时，两种算法均能达到Antoniadis等人'20先前保证的性能水平。