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English(EN) Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems

新框架助力解决未知干扰下的在线实验设计

作者 PulseAugur 编辑部 · [2 个来源] ·

一项新的研究论文介绍了一个在干扰机制未知的情况下选择在线实验设计的框架。该方法称为鲁棒设计选择，根据最坏情况规划风险评估了六种不同的设计，并考虑了偏差、方差、成本和估计量不匹配等因素。该论文提供了理论保证，并在公共数据集上展示了其应用，根据 Criteo 广告、Open Bandit 和 KuaiRand 的各自风险推荐了具体设计。 AI

排序理由 该集群包含一篇发表在 arXiv 上的研究论文，详细介绍了一个新的实验设计框架。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新框架助力解决未知干扰下的在线实验设计

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该集群包含一篇发表在 arXiv 上的研究论文，详细介绍了一个新的实验设计框架。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Prashant Shekhar, Caroline Howard ·

    在广告、推荐和会员体验系统中的干扰下选择在线实验设计

    arXiv:2605.25290v1 Announce Type: cross Abstract: Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through budgets, inventory, producer exposure, graph spillov…

  2. arXiv stat.ML TIER_1 English(EN) · Caroline Howard ·

    在广告、推荐和会员体验系统中的干扰下选择在线实验设计

    Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through budgets, inventory, producer exposure, graph spillovers, or temporal carryover, making the randomizati…

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