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English(EN) The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits

新程序优化了对决赌博机中孔多塞获胜者的识别

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的程序，用于在大规模随机对决赌博机中识别孔多塞获胜者。该方法通过利用完整的差距矩阵来改进现有技术，该矩阵包含所有成对比较的信息，而不仅仅是涉及获胜者的比较。该程序实现了高概率、实例相关的样本复杂度保证，并被证明是最优的，揭示了一种平衡信息比较定位与验证成本的通用采样复杂度形式。 AI

影响 这项研究推进了对赌博机算法的理论理解，可能导致更高效的AI系统在复杂环境中进行决策。

排序理由 该集群包含一篇学术论文，详细介绍了机器学习特定领域的新程序和理论结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新程序优化了对决赌博机中孔多塞获胜者的识别

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该集群包含一篇学术论文，详细介绍了机器学习特定领域的新程序和理论结果。[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) · El Mehdi Saad (CC-UM6P-Rabat, UM6P), Victor Thuot (MISTEA), Nicolas Verzelen (MISTEA) ·

    在二元对决赌博机中孔多塞获胜者识别的采样复杂度

    arXiv:2603.15189v2 Announce Type: replace-cross Abstract: We study best-arm identification in large-scale stochastic dueling bandits under the sole assumption that a Condorcet winner exists, i.e., an arm that wins each noisy pairwise comparison with probability at least $1/2$. We…

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