3 citations · 8 across the 11 of their papers we have counts for
3 papers · 1 filter
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…