English(EN) Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization

新算法利用凹度实现高效上下文老虎机优化 · arXiv论文

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员开发了一种新的上下文老虎机算法，该算法利用奖励函数中的凹度信息来提高优化效率。该算法在最近的一篇arXiv论文中进行了详细介绍，它使用约束高斯过程后验来模拟对于每个观察到的上下文而言已知相对于动作是凹的奖励。所提出的方法将回归样条基与这种约束GP后验相结合，创建了一个形状约束估计器，从而得到一个UCB算法，与标准的GP-UCB相比，在凹度是重要因素时，具有改进的遗憾保证。在基准问题和华法林剂量应用上的实验显示累积遗憾显著降低。 AI

影响这项研究可能通过改进上下文老虎机算法，从而在医学和工程等领域实现更高效的决策。

排序理由这是一篇详细介绍针对特定机器学习问题的创新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Kevin Li, Eric Laber · 2026-10-22 04:00

Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization

arXiv:2503.10836v2 Announce Type: replace-cross Abstract: The contextual bandit models sequential decision-making problems in which rewards depend on both the chosen action and observed context. In many domains such as medicine, business, and engineering, prior knowledge provides…

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Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization

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