Zurück e d 04.07.2023 - 04.07.2023 | Saarbrücken Abstract: Contextual bandit problems model the inherent cost of learning in personalized decision-making in new environments, whether in marketing, healthcare, or revenue management. Specifically, the cost is characterized by the optimal growth rate of the regret in cumulative rewards compared to an optimal policy given full prior knowledge of the environment. Naturally, the optimal rate should depend on how complex the underlying supervised learning problem is, namely how much can observing rewards in one context tell us about mean rewards in another context.

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