185 citations · 375 across the 16 of their papers we have counts for
17 papers
Dynamic Regret in Online Convex Optimization with Indicator Switching Costs
Naram Mhaisen, George Iosifidis
We study dynamic regret in online convex optimization with an \emph{indicator switching cost}: a fixed penalty incurred whenever two consecutive decisions differ. This captures sta…
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…
Adaptive Online Non-stochastic Control
Naram Mhaisen, George Iosifidis
We tackle the problem of Non-stochastic Control (NSC) with the aim of obtaining algorithms whose policy regret is proportional to the difficulty of the controlled environment. Name…
Optimistic No-regret Algorithms for Discrete Caching
Naram Mhaisen, Abhishek Sinha, Georgios Paschos +1
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 pre…
Online Caching with no Regret: Optimistic Learning via Recommendations
Naram Mhaisen, George Iosifidis, Douglas Leith
The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among ot…
Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous multi-RAT Networks
Mhd Saria Allahham, Alaa Awad Abdellatif, Naram Mhaisen +3
The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple…
Online Caching with Optimistic Learning
Naram Mhaisen, George Iosifidis, Douglas Leith
The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among ot…
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Jingjing Zheng, Kai Li, Naram Mhaisen +3
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT). On…
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey
Alaa Awad Abdellatif, Naram Mhaisen, Zina Chkirbene +3
The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This c…
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data
Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed +4
Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. I…
Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
Emna Baccour, Naram Mhaisen, Alaa Awad Abdellatif +4
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to ro…
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data
Naram Mhaisen, Alaa Awad, Amr Mohamed +2
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devic…