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Nonparametric Contextual Bandits in an Unknown Metric Space
Consider a nonparametric contextual multi-arm bandit problem where each ...
08/03/2019 ∙ by Nirandika Wanigasekara, et al. ∙
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Thompson Sampling for CVaR Bandits
Risk awareness is an important feature to formulate a variety of real wo...
12/10/2020 ∙ by Dorian Baudry, et al. ∙
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Secure-UCB: Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification
This paper studies bandit algorithms under data poisoning attacks in a b...
02/15/2021 ∙ by Anshuka Rangi, et al. ∙
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Compliance-Aware Bandits
Motivated by clinical trials, we study bandits with observable non-compl...
02/09/2016 ∙ by Nicolás Della Penna, et al. ∙
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Recovering Bandits
We study the recovering bandits problem, a variant of the stochastic mul...
10/31/2019 ∙ by Ciara Pike-Burke, et al. ∙
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Predictive Bandits
We introduce and study a new class of stochastic bandit problems, referr...
04/02/2020 ∙ by Simon Lindståhl, et al. ∙
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Thresholded LASSO Bandit
In this paper, we revisit sparse stochastic contextual linear bandits. I...
10/22/2020 ∙ by Kaito Ariu, et al. ∙
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Bandits for BMO Functions
07/17/2020 ∙ by Tianyu Wang, et al. ∙
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We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function. BMO functions are allowed to be discontinuous and unbounded, and are useful in modeling signals with infinities in the do-main. We develop a toolset for BMO bandits, and provide an algorithm that can achieve poly-log δ-regret – a regret measured against an arm that is optimal after removing a δ-sized portion of the arm space.
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