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English(EN) Online Learning with Ordered Experts (via Two-point Feedback)

新的在线学习算法利用两点反馈解决对抗性设置

研究人员开发了用于对抗性在线学习的新算法,该算法在一个具有全序动作空间的环境中运行。重点在于一种两点反馈机制,即学习者每轮进行两次推荐,并承担平均损失。对于分离单调问题,所提出的方法实现了接近全信息界限的遗憾值;对于BV问题,它实现了接近最优的遗憾值。动态定价中的一个应用表明,这种双选项方法在对数因子范围内可与全信息速率相媲美。 AI

影响 引入了带有专家建议的新型在线学习算法,有可能改善动态定价等领域的决策。

排序理由 在arXiv上发表的学术论文,详细介绍了新的在线学习算法。[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 →

新的在线学习算法利用两点反馈解决对抗性设置

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在arXiv上发表的学术论文,详细介绍了新的在线学习算法。[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) · Shiliang Zuo ·

    在线学习与有序专家(通过两点反馈)

    arXiv:2611.07629v1 Announce Type: new Abstract: We study adversarial online learning with expert recommendations in a totally ordered action space, where the experts' recommendations may change and be reordered arbitrarily. Our focus is two-point feedback: on each round the learn…