3 citations · 8 across the 11 of their papers we have counts for
12 papers
Does Feedback Help in Bandits with Arm Erasures?
Merve Karakas, Osama Hanna, Lin F. Yang +1
We study a distributed multi-armed bandit (MAB) problem over arm erasure channels, motivated by the increasing adoption of MAB algorithms over communication-constrained networks. I…
Learning for Bandits under Action Erasures
Osama Hanna, Merve Karakas, Lin F. Yang +1
We consider a novel multi-arm bandit (MAB) setup, where a learner needs to communicate the actions to distributed agents over erasure channels, while the rewards for the actions ar…
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
Osama A. Hanna, Merve Karakas, Lin F. Yang +1
Multi-Armed Bandit (MAB) systems are witnessing an upswing in applications within multi-agent distributed environments, leading to the advancement of collaborative MAB algorithms.…
Common Information Dimension
Osama Hanna, Xinlin Li, Suhas Diggavi +1
The exact common information between a set of random variables is defined as the minimum entropy of a shared random variable that allows for the exact distributive si…
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
Osama A. Hanna, Lin F. Yang, Christina Fragouli
In this paper, we address the stochastic contextual linear bandit problem, where a decision maker is provided a context (a random set of actions drawn from a distribution). The exp…
Differentially Private Stochastic Linear Bandits: (Almost) for Free
Osama A. Hanna, Antonious M. Girgis, Christina Fragouli +1
In this paper, we propose differentially private algorithms for the problem of stochastic linear bandits in the central, local and shuffled models. In the central model, we achieve…
Learning in Distributed Contextual Linear Bandits Without Sharing the Context
Osama A. Hanna, Lin F. Yang, Christina Fragouli
Contextual linear bandits is a rich and theoretically important model that has many practical applications. Recently, this setup gained a lot of interest in applications over wirel…
Solving Multi-Arm Bandit Using a Few Bits of Communication
Osama A. Hanna, Lin F. Yang, Christina Fragouli
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…
Quantizing data for distributed learning
Osama A. Hanna, Yahya H. Ezzeldin, Christina Fragouli +1
We consider machine learning applications that train a model by leveraging data distributed over a trusted network, where communication constraints can create a performance bottlen…
On Distributed Quantization for Classification
Osama A. Hanna, Yahya H. Ezzeldin, Tara Sadjadpour +2
We consider the problem of distributed feature quantization, where the goal is to enable a pretrained classifier at a central node to carry out its classification on features that…
Random Access Schemes in Wireless Systems With Correlated User Activity
Anders Ellersgaard Kalør, Osama A. Hanna, Petar Popovski
Traditional random access schemes are designed based on the aggregate process of user activation, which is created on the basis of independent activations of the users. However, in…
Degrees of Freedom in Cached MIMO Relay Networks With Multiple Base Stations
Osama A. Hanna, Amr El-Keyi, Mohammed Nafie
The ability of physical layer relay caching to increase the degrees of freedom (DoF) of a single cell was recently illustrated. In this paper, we extend this result to the case of…