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新算法在连续动作上下文老虎机中强制执行安全约束

研究人员开发了一种名为高概率约束UCB的新算法,用于具有连续动作的上下文老虎机问题。该算法通过对已实现动作成本强制执行高概率约束来解决安全问题,这对于临床试验和自主系统等应用至关重要,因为不安全的决策可能导致严重后果。与之前关注预期成本的方法不同,这种方法考虑了结果的可变性,提供了更强的安全保证。该算法在线性模型中实现了严格的遗憾界限,并扩展到更一般的函数类,实验结果表明与现有基线相比,其在减少安全违规方面非常有效。 AI

影响 增强了顺序决策系统中安全性的保证,这对于现实世界的AI应用至关重要。

排序理由 关于上下文老虎机新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新算法在连续动作上下文老虎机中强制执行安全约束

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关于上下文老虎机新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Spyros Dragazis, Aldo Pacchiano ·

    安全设计:连续动作上下文老虎机的实际成本约束

    arXiv:2608.26755v1 Announce Type: new Abstract: Contextual bandits are a standard framework for sequential decision-making under uncertainty, with applications in clinical trials, dosage selection, recommendation systems, and autonomous systems. Safety is central in many of these…