English(EN) Online Convex Optimization with Dueling Feedback

新方法利用成对反馈解决在线凸优化问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-18 04:00

研究人员引入了一种新颖的在线凸优化方法，该方法利用对偶（成对比较）反馈。该方法将二元偏好数据转换为近似梯度，从而可以应用标准的一阶优化技术。所提出的约简成功地转移了遗憾保证，为该特定场景建立了第一个已知结果，包括静态、自适应和动态遗憾的 O(T^{3/4}) 界限。进一步的改进为平滑目标产生了 O(T^{2/3}) 的速率，为强凸函数产生了 O(sqrt(T log T)) 的速率。 AI

影响为与机器学习相关的优化问题引入了新的理论框架。

排序理由详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yiyang Lu, Hareshkumar Jadav, Mohammad Pedramfar, Ranveer Singh, Vaneet Aggarwal · 2026-08-18 04:00

具有竞争性反馈的在线凸优化

arXiv:2608.15050v1 Announce Type: new Abstract: We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried points. While dueling feedback is well understood in discrete or stochastic set…

本文如何被排名

报道来源 [1]

具有竞争性反馈的在线凸优化

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