no code implementations • 22 Apr 2024 • Jung-hun Kim, Milan Vojnovic, Se-Young Yun
In this study, we consider the infinitely many-armed bandit problems in a rested rotting setting, where the mean reward of an arm may decrease with each pull, while otherwise, it remains unchanged.
no code implementations • 30 May 2022 • Jung-hun Kim, Se-Young Yun
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.
1 code implementation • 31 Jan 2022 • Jung-hun Kim, Milan Vojnovic, Se-Young Yun
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 $\varrho=o(1)$.
1 code implementation • 13 Dec 2021 • Jung-hun Kim, Milan Vojnovic
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.
1 code implementation • 3 Mar 2017 • Jung-hun Kim, Se-Young Yun, Minchan Jeong, Jun Hyun Nam, Jinwoo Shin, Richard Combes
This implies that classical approaches cannot guarantee a non-trivial regret bound.