7 papers
SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search
Noor Khial, Naram Mhaisen, Loay Ismail +1
Unmanned Aerial Vehicles (UAVs) equipped with radar sensors are deployed for target search missions in diverse environments, where targets exhibit characteristic signatures (e.g.,…
Partially Lazy Gradient Descent for Smoothed Online Learning
Naram Mhaisen, George Iosifidis
We introduce \textsc{-lazyGD}, an online learning algorithm that bridges the gap between greedy Online Gradient Descent (OGD, for ) and lazy GD/dual-averaging (for $k{=}T…
On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning
Naram Mhaisen, George Iosifidis
We revisit the Follow the Regularized Leader (FTRL) framework for Online Convex Optimization (OCO) over compact sets, focusing on achieving dynamic regret guarantees. Prior work ha…
Optimistic Learning for Communication Networks
George Iosifidis, Naram Mhaisen, Douglas J. Leith
AI/ML-based tools are at the forefront of resource management solutions for communication networks. Deep learning, in particular, is highly effective in facilitating fast and high-…
Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services
Menna Helmy, Alaa Awad Abdellatif, Naram Mhaisen +2
The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation o…
Optimistic Online Non-stochastic Control via FTRL
Naram Mhaisen, George Iosifidis
This paper brings the concept of ``optimism" to the new and promising framework of online Non-stochastic Control (NSC). Namely, we study how NSC can benefit from a prediction oracl…