English(EN) Risk-Sensitive Non-Parametric and Kernel Bandits

新的赌徒算法结合了风险敏感的累积前景理论

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了用于非参数和核赌徒的新算法，这些算法结合了累积前景理论（CPT）以进行风险敏感的决策。这些名为非参数CPT赌徒（NPCB）和核CPT赌徒的算法旨在优化基于CPT的遗憾，而不是传统的预期回报。所提出的方法实现了与现有非参数赌徒算法相当的遗憾界限，这表明结合CPT风险敏感性不会产生额外的顺序遗憾成本。 AI

影响在机器学习中引入了用于风险敏感决策的新型算法，可能影响风险规避至关重要的领域。

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在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Devdan Dey, Sujoy Bhore, Avishek Ghosh · 2026-11-05 05:00

风险敏感的非参数与核方法老虎机算法

arXiv:2611.06489v1 Announce Type: cross Abstract: We study non-parametric bandits in which arms are evaluated using Cumulative Prospect Theory (CPT) rather than expected reward. We consider continuous action spaces with arm-dependent noise and propose Non-Parametric CPT Bandits (…

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风险敏感的非参数与核方法老虎机算法

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