4 papers
Efficient and Optimal No-Regret Caching under Partial Observation
Younes Ben Mazziane, Francescomaria Faticanti, Sara Alouf +1
Online learning algorithms have been successfully used to design caching policies with sublinear regret in the total number of requests, with no statistical assumption about the re…
An Online Gradient-Based Caching Policy with Logarithmic Complexity and Regret Guarantees
Damiano Carra, Giovanni Neglia
Commonly used caching policies, such as LRU (Least Recently Used) or LFU (Least Frequently Used), exhibit optimal performance only under specific traffic patterns. Even advanced ma…
Optimistic Online Caching for Batched Requests
Francescomaria Faticanti, Giovanni Neglia
In this paper we study online caching problems where predictions of future requests, e.g., provided by a machine learning model, are available. Typical online optimistic policies a…
No-Regret Caching with Noisy Request Estimates
Younes Ben Mazziane, Francescomaria Faticanti, Giovanni Neglia +1
Online learning algorithms have been successfully used to design caching policies with regret guarantees. Existing algorithms assume that the cache knows the exact request sequence…