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English(EN) Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling

新的VOCEM估计器降低了离轨策略评估中的方差

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为方差最优结合效应模型（VOCEM）的新估计器，用于上下文老虎机策略的离轨策略评估。该方法旨在通过在现有估计器OffCEM和双重稳健（DR）之间进行插值来降低与动作级重要性加权相关的高方差。VOCEM选择一个插值系数来最小化方差，从而得到一个方差不大于OffCEM或DR的估计器。在合成数据和基准测试上的实验表明，VOCEM在各种条件下均优于OffCEM和DR，显示出改进的稳定性和经验鲁棒性。 AI

影响 提高了上下文老虎机策略评估的稳定性和鲁棒性，可能带来更可靠的强化学习系统。

排序理由 该集群包含一篇详细介绍离轨策略评估新估计器的研究论文。[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 2 个来源。 我们如何撰写摘要 →

新的VOCEM估计器降低了离轨策略评估中的方差

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该集群包含一篇详细介绍离轨策略评估新估计器的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Nicol\`o Felicioni, Michael Benigni, Maurizio Ferrari Dacrema, Paolo Cremonesi ·

    具有联合效应建模的方差最优策略外评估

    arXiv:2610.08677v1 Announce Type: new Abstract: Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly robust (DR) estimation remains unbiased under common support but retains these h…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    具有联合效应建模的方差最优策略外评估

    Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly robust (DR) estimation remains unbiased under common support but retains these high-variance action-level weights. A prior estim…

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