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- W3212763935 abstract "We give an $(varepsilon,delta)$-differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $Oleft(left(sum_{ain [k]:Delta_a>0}frac{log T}{Delta_a}right)+frac{ksqrt{logfrac{1}{delta}}log T}{varepsilon}right)$, and a distribution-independent regret of $Oleft(sqrt{kTlog T}+frac{ksqrt{logfrac{1}{delta}}log T}{varepsilon}right)$, where $T$ is the number of rounds, $Delta_a$ is the suboptimality gap of the arm $a$, and $k$ is the total number of arms. Our upper bound almost matches the regret of the best known algorithms for the centralized model, and significantly outperforms the best known algorithm in the local model." @default.
- W3212763935 created "2021-11-22" @default.
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- W3212763935 date "2021-12-06" @default.
- W3212763935 modified "2023-09-24" @default.
- W3212763935 title "Differentially Private Multi-Armed Bandits in the Shuffle Model" @default.
- W3212763935 hasPublicationYear "2021" @default.
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