We consider a version of the classical stochastic Multi-Armed bandit problem in which the number of arms is large compared to the time horizon, with the goal of minimizing the cumulative regret. Here, the mean-reward (or value) of newly chosen arms is assumed to be i.i.d. We further ...
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) \\varrho=o(1) . We show ... JH Ki...
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The United States was now a player in World War II, which meant the introduction of gas rationing. Gas rationing had little to do with a shortage; what the United States armed forces needed was rubber, so nonessential rubber usage (like car tires) had to go. In order to stop people fr...
This setting can be viewed as an instance of a stochastic K-armed bandit problem where the location of the arms (the K unknown valuations) must be learned as well. In the distribution-free case, we show that our setting is just as hard as K-armed stochastic bandits: we prove that no ...
Many-armed banditsRegret minimizationWe consider a variant of the Multi-Armed Bandit problem which involves a large pool of a priori identical arms (or items). Each arm is associated with a deterministic value, which is sampled from a probability distribution with unknown maximal value, and is ...
We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous algorithms for this setting were designed for ...
Coverage-based Greybox Fuzzing (CGF) is a practical and effective solution for finding bugs and vulnerabilities in software. A key challenge of CGF is how to select conducive seeds and allocate accurate energy. To address this problem, we propose a novel