185 citations · 375 across the 16 of their papers we have counts for
9 papers · 1 filter
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…
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 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…
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…
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…
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…