Optimistic No-regret Algorithms for Discrete Caching. (arXiv:2208.06414v1 [cs.LG])

We take a systematic look at the problem of storing whole files in a cache
with limited capacity in the context of optimistic learning, where the caching
policy has access to a prediction oracle (provided by, e.g., a Neural Network).
The successive file requests are assumed to be generated by an adversary, and
no assumption is made on the accuracy of the oracle. In this setting, we
provide a universal lower bound for prediction-assisted online caching and
proceed to design a suite of policies with a range of performance-complexity
trade-offs. All proposed policies offer sublinear regret bounds commensurate
with the accuracy of the oracle. Our results substantially improve upon all
recently-proposed online caching policies, which, being unable to exploit the
oracle predictions, offer only $O(sqrt{T})$ regret. In this pursuit, we
design, to the best of our knowledge, the first comprehensive optimistic
Follow-the-Perturbed leader policy, which generalizes beyond the caching
problem. We also study the problem of caching files with different sizes and
the bipartite network caching problem. Finally, we evaluate the efficacy of the
proposed policies through extensive numerical experiments using real-world
traces.