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Jan, 2017
对抗多臂赌博机中实现隐私保护
Achieving Privacy in the Adversarial Multi-Armed Bandit
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Aristide C. Y. Tossou, Christos Dimitrakakis
TL;DR
本文提出了一种结合拉普拉斯机制和EXP3的算法，在对抗性赌徒环境中实现ε差分隐私，并将最佳已知遗憾界从O(T^(3/4))提高到了O(T^(2/3))，同时达到了O(√T ln T/ε)的决策精度，其在自适应对手中具有良好的鲁棒性，并进行了实验验证。
Abstract
In this paper, we improve the previously best known
regret bound
to achieve $\epsilon$-
differential privacy
in oblivious
adversarial bandits
→