Osama A. Hanna — accepted AI-conference papers

2024

Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels

Osama A Hanna, Merve Karakas, Lin Yang, Christina Fragouli

AISTATS 2024poster

Multi-Armed Bandit (MAB) systems are witnessing an upswing in applications within multi-agent distributed environments, leading to the advancement of collaborative MAB algorithms. In such settings, communication between agents executing actions and the primary learner making decisions can hinder the…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2022

Solving Multi-Arm Bandit Using a Few Bits of Communication

Osama A. Hanna, Lin Yang, Christina Fragouli

AISTATS 2022poster

The multi-armed bandit (MAB) problem is an active learning framework that aims to select the best among a set of actions by sequentially observing rewards. Recently, it has become popular for a number of applications over wireless networks, where communication constraints can form a bottleneck. Exis…

Cited by 21 Source ↗PDF ↗Scholar ↗