Robust Learning-Augmented Caching: An Experimental Study

Robust Learning-Augmented Caching: An Experimental Study

International Conference on Machine Learning [ICML]

2021

We comprehensively evaluate learning-augmented algorithms on real-world caching datasets and state-of-the-art machine-learned predictors. We show that a straightforward method – blindly following either a predictor or a classical robust algorithm, and switching whenever one becomes worse than the other – has only a low overhead over a well-performing predictor, while competing with classical methods when the coupled predictor fails, thus providing a cheap worst-case insurance.

1920-1930

http://proceedings.mlr.press/v139/chledowski21a.html