We consider a fair resource allocation problem in the no-regret setting against an
unrestricted adversary. The objective is to allocate resources equitably among several …

O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-
Cloud) constitute a paradigm shift expected to bring unprecedented performance gains …

In this paper, we investigate 'optimistic'online caching policies, distinguished by their use of
future request predictions derived, for example, from machine learning models. Traditional …

This paper brings the concept of" optimism" to the new and promising framework of online
Non-stochastic Control (NSC). Namely, we study how can NSC benefit from a prediction …

The commonly used caching policies, such as LRU or LFU, exhibit optimal performance only
for specific traffic patterns. Even advanced Machine Learning-based methods, which detect …

Online learning algorithms have been successfully used to design caching policies with
regret guarantees. Existing algorithms assume that the cache knows the exact request …

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 …

We revisit the classic problem of optimal subset selection in the online learning set-up.
Assume that the set $[N] $ consists of $ N $ distinct elements. On the $ t $ th round, an …