Partially Lazy Gradient Descent for Smoothed Online Learning

May 1, 2026·
Naram Mhaisen
Naram Mhaisen
,
George Iosifidis
Abstract
We propose a partially lazy variant of gradient descent for smoothed online learning, balancing switching costs against regret in dynamic environments.
Type
Publication
In Proceedings of the 29th International Conference on Artificial Intelligence and Statistics (AISTATS)
publications
Online Learning Smoothed Online Learning
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).