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Found 3 papers, 1 papers with code
Combinatorial Bandits for Maximum Value Reward Function under Max Value-Index Feedback
We propose an algorithm and provide a regret bound for problem instances with stochastic arm outcomes according to arbitrary distributions with finite supports.
Doubly Adversarial Federated Bandits
For the bandit feedback setting, we propose a near-optimal federated bandit algorithm called FEDEXP3.
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits
We show that with suitable regularizers and communication protocols, a collaborative multi-agent \emph{follow-the-regularized-leader} (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph.
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