English(EN) Two-Action Apple Tasting with Switching Costs

苹果品尝问题的遗憾界限被发现为 \u221aT

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-02 16:28

研究人员分析了具有转换成本的“二元行动苹果品尝问题”，该场景与机器学习算法相关。他们发现该问题的预期遗憾界限为 $\sqrt{T}$，优于先前假设的 $\widetilde O(T^{2/3})$ 界限。这一发现消除了反馈图算法分类中一个潜在的障碍。 AI

影响为一类学习算法建立了更严格的理论界限，可能影响未来的算法设计。

排序理由该集群包含一篇详细介绍机器学习理论结果的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Tommaso Cesari, Roberto Colomboni · 2026-06-03 04:00

Two-Action Apple Tasting with Switching Costs

arXiv:2606.03851v1 Announce Type: new Abstract: We study the two-action apple-tasting problem with switching costs against an oblivious adversary. In an equivalent normalized formulation, at each round the learner chooses between a revealing action and a blind action: the reveali…
arXiv cs.LG TIER_1 English(EN) · Roberto Colomboni · 2026-06-02 16:28

具有转换成本的二元苹果品鉴

We study the two-action apple-tasting problem with switching costs against an oblivious adversary. In an equivalent normalized formulation, at each round the learner chooses between a revealing action and a blind action: the revealing action gives reward $0$ and reveals the hidde…