English(EN) Efficient contextual partial monitoring with general function approximation

新框架增强了具有部分反馈的顺序学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一个新的框架，用于具有部分反馈的顺序学习，采用了估计到决策（E2D）元算法。该方法利用在线回归预言机，根据历史数据有效地估计结果分布。该研究提出了两种计算上高效的探索规则，一种基于epsilon-greedy，另一种基于逆差距加权，以实现最优遗憾阶数。该方法在合成标签高效预测游戏和LLM路由任务上进行了评估，与现有方法相比表现出优越的性能。 AI

影响这项研究可以提高LLM路由和其他具有部分反馈的顺序学习任务的效率。

排序理由该集群包含一篇详细介绍新算法及其评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Kapilan Balagopalan, Hao Qin, Kwang-Sung Jun, Chicheng Zhang · 2026-10-15 04:00

高效上下文局部监控与通用函数逼近

arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round $t \in [T]$, both player and environment observe a context and player chooses an …

本文如何被排名

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高效上下文局部监控与通用函数逼近

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