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• W2946721915 abstract "Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection. In these applications, a reneging phenomenon, where participants may disengage from future interactions after observing an unsatisfiable outcome, is rather prevalent. To address the above issue, this paper proposes a model of heteroscedastic linear bandits with reneging, which allows each participant to have a distinct satisfaction level, with any interaction outcome falling short of that level resulting in that participant reneging. Moreover, it allows the variance of the outcome to be context-dependent. Based on this model, we develop a UCB-type policy, namely HR-UCB, and prove that it achieves $mathcal{O}big(sqrt{{T}(log({T}))^{3}}big)$ regret. Finally, we validate the performance of HR-UCB via simulations." @default.
• W2946721915 created "2019-05-29" @default.
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• W2946721915 date "2018-10-29" @default.
• W2946721915 modified "2023-09-22" @default.
• W2946721915 title "Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging" @default.
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• W2946721915 hasPublicationYear "2018" @default.
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