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English(EN) Best Arm Identification with Minimal Regret

机器学习中引入了具有最小遗憾的新型BAI

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了一个名为“具有最小遗憾的最佳臂识别”（BAI）的新问题，该问题结合了在多臂老虎机问题中识别最佳臂与最小化累积遗憾的目标。该研究侧重于单参数指数族，并使用信息论技术建立了预期累积遗憾的下界。此外，一个不可能结果突显了固定置信度BAI中遗憾与样本复杂度之间的权衡，而提出的Double KL-UCB算法在置信度降低时表现出渐近最优性。 AI

排序理由 该集群包含一篇学术论文，详细介绍了机器学习中的新问题表述和算法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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机器学习中引入了具有最小遗憾的新型BAI

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该集群包含一篇学术论文，详细介绍了机器学习中的新问题表述和算法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin ·

    最小遗憾的最佳手臂识别

    arXiv:2409.18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret. This variant of the multi-armed bandit problem elegantly amalgamat…

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