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Found 5 papers, 3 papers with code
Rotting Infinitely Many-armed Bandits beyond the Worst-case Rotting: An Adaptive Approach
In this study, we consider the infinitely many armed bandit problems in rotting environments, where the mean reward of an arm may decrease with each pull, while otherwise, it remains unchanged.
Adversarial Bandits against Arbitrary Strategies
We study the adversarial bandit problem against arbitrary strategies, in which $S$ is the parameter for the hardness of the problem and this parameter is not given to the agent.
Rotting Infinitely Many-armed Bandits
We consider the infinitely many-armed bandit problem with rotting rewards, where the mean reward of an arm decreases at each pull of the arm according to an arbitrary trend with maximum rotting rate $\varrho=o(1)$.
Scheduling Servers with Stochastic Bilinear Rewards
In this paper, we study scheduling in multi-class, multi-server queueing systems with stochastic rewards of job-server assignments following a bilinear model in feature vectors characterizing jobs and servers.
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
This implies that classical approaches cannot guarantee a non-trivial regret bound.
