Online Caching with no Regret: Optimistic Learning via Recommendations
An optimistic learning framework for online caching that leverages recommendations to improve regret without sacrificing worst-case guarantees.
Optimistic Online Non-stochastic Control via FTRL
Optimistic FTRL controllers for online non-stochastic control with improved performance under accurate predictions.
Optimistic No-regret Algorithms for Discrete Caching
No-regret algorithms for discrete caching that integrate untrusted predictions for improved performance.
Online Caching with Optimistic Learning
Online caching with optimistic learning, exploiting predictions while keeping no-regret guarantees.