Robust Learning-Augmented Caching: An Experimental Study

Tytuł:
Robust Learning-Augmented Caching: An Experimental Study
Konferencja:
International Conference on Machine Learning [ICML]
Rok:
2021

Opis:
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.

Strony:
1920--1930

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