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新算法在具有非Lipschitz需求的上下文定价中实现了最优遗憾

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为Conservative-Markdown Redirect-UCB Pricing的新算法，以应对上下文动态定价的挑战。该算法旨在处理非Lipschitz的需求曲线，这些曲线具有任意的跳跃和原子，而这在以前阻碍了定价算法。新方法实现了\tilde O(T^{2/3})的最优遗憾，优于先前的方法，并缩小了线性估值上下文定价的理论理解差距。 AI

影响 提高了对复杂需求场景下定价算法的理论理解，可能影响电子商务和推荐系统。

排序理由 这是一篇发表在arXiv上的研究论文，详细介绍了一种新的上下文定价算法。

在 arXiv stat.ML 阅读 →

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新算法在具有非Lipschitz需求的上下文定价中实现了最优遗憾

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jianyu Xu, Yu-Xiang Wang ·

    最优上下文定价在不可知非Lipschitz需求下

    arXiv:2605.05609v1 Announce Type: new Abstract: We study contextual dynamic pricing with linear valuations and bounded-support agnostic noise, whose induced demand curve may be non-Lipschitz with arbitrary jumps and atoms. Such discontinuities break the cross-context interpolatio…

  2. arXiv stat.ML TIER_1 English(EN) · Yu-Xiang Wang ·

    最优上下文定价与不可知非Lipschitz需求

    We study contextual dynamic pricing with linear valuations and bounded-support agnostic noise, whose induced demand curve may be non-Lipschitz with arbitrary jumps and atoms. Such discontinuities break the cross-context interpolation arguments used by smooth-demand pricing algori…

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