On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning

Jul 13, 2025·
Naram Mhaisen
Naram Mhaisen
,
George Iosifidis
Abstract
We analyze the dynamic regret of Follow-the-Regularized-Leader (FTRL) under optimistic predictions, introducing a history-pruning technique that yields improved bounds.
Type
Publication
In Proceedings of the 42nd International Conference on Machine Learning (ICML)
publications
Online Learning Optimistic Learning Dynamic Regret
Naram Mhaisen
Authors
Naram Mhaisen
Postdoctoral Researcher

Naram is a Postdoctoral Researcher with the Networked Systems Group at the Faculty of Electrical Engineering, Mathematics, and Computer Science, TU Delft, The Netherlands. His research focuses on online learning techniques with applications in resource management across communication systems.

He received the B.Sc. degree in Computer Engineering from Qatar University in 2017 with the Order of Excellence award, and the M.Sc. degree with Distinction in Computing from Qatar University in 2020, supervised by Prof. Mohsen Guizani. He completed his Ph.D. at TU Delft in 2025, supervised by Dr. George Iosifidis, with a thesis on Optimistic Learning with Applications to Caching Networks.

His work has been recognized by the Microsoft Imagine Cup award (2017) and a NEC Europe Research Fellowship (2022).