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English(EN) Replicable Dueling Bandits

新算法确保可复现的对决赌徒具有亚线性遗憾

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了可复现对决赌徒的新算法，这是一种系统，其中具有共享随机性的两次执行必须以高概率在每一轮中选择相同的有序对。基于“Beat-the-Mean”方法，这些算法在放宽的随机传递性和随机三角不等式下实现了亚线性遗憾。对于线性对决赌徒，另一种算法提供了改进的遗憾界限。在合成数据和语言模型偏好上的实验证明了这些可复现赌徒算法的有效性。 AI

影响 为AI系统中的偏好学习和决策引入了新颖的算法。

排序理由 该集群包含一篇详细介绍特定机器学习问题新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新算法确保可复现的对决赌徒具有亚线性遗憾

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该集群包含一篇详细介绍特定机器学习问题新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rohan Deb, Aadirupa Saha, Udaya Ghai, Karan Singh, Arindam Banerjee ·

    可复现的二元策略博弈

    arXiv:2610.20908v1 Announce Type: cross Abstract: Dueling bandits choose each comparison based on earlier noisy outcomes, so two runs on the same preferences can compare different pairs. We study replicable dueling bandits, where two executions with shared internal randomness and…

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