研究人员为在具有全老虎机反馈的预算组合多臂老虎机中实现精英公平性引入了一个新颖的框架。这种新方法将 Shapley 值概念扩展到 K-Shapley 值，它量化了代理在有限集合大小内的边际贡献。提出的 K-SVFair-FBF 算法自适应地估计此 K-Shapley 值，在与联邦学习和社会影响力最大化相关的数据集上展示了改进的公平性和性能。 AI
影响 为老虎机问题引入了新的公平性指标和算法，有可能改善复杂系统中的资源分配。
排序理由 介绍新算法框架和理论成果的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.00762v1 Announce Type: new Abstract: We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandi…
We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting significantly mor…