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English(EN) Learning to Allocate Resources with Censored Feedback

新算法解决审查反馈下的资源分配问题

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了新的算法RA-UCB和MG-UCB，以解决带有审查反馈的在线资源分配问题。该问题涉及在多个臂之间分配预算，其中只有在满足特定激活和预算阈值条件时才能收到奖励。这些算法旨在平衡探索和利用以最大化累积奖励，理论保证显示最坏情况下的遗憾界限为$\Omega(\sqrt{T})$。在真实数据集上的实验验证了这些方法的有效性。 AI

影响 为与从不完整反馈中学习的AI系统相关的资源分配问题引入了新颖的算法方法。

排序理由 详细介绍新算法和理论结果的学术论文。[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 →

新算法解决审查反馈下的资源分配问题

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Tool
详细介绍新算法和理论结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
3 days old
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Giovanni Montanari, C\^ome Fiegel, Corentin Pla, Aadirupa Saha, Vianney Perchet ·

    Learning to Allocate Resources with Censored Feedback

    arXiv:2602.06565v2 Announce Type: replace Abstract: We study an online resource allocation problem in which at each round, a budget $B$ must be allocated across $K$ arms under censored feedback. An arm yields a reward if and only if two conditions are satisfied: (i) the arm is ac…

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