  Lykouris and Vassilvitskii (ICML 2018) introduce a model of online caching
with machine-learned advice, where each page request additionally comes with a
prediction of when that page will next be requested. In this model, a natural
goal is to design algorithms that (1) perform well when the advice is accurate
and (2) remain robust in the worst case a la traditional competitive analysis.
Lykouris and Vassilvitskii give such an algorithm by adapting the Marker
algorithm to the learning-augmented setting. In a recent work, Rohatgi (SODA
2020) improves on their result with an approach also inspired by randomized
marking. We continue the study of this problem, but with a somewhat different
approach: We consider combining the BlindOracle algorithm, which just na\"ively
follows the predictions, with an optimal competitive algorithm for online
caching in a black-box manner. The resulting algorithm outperforms all existing
approaches while being significantly simpler. Moreover, we show that combining
BlindOracle with LRU is in fact optimal among deterministic algorithms for this
problem.
