研究人员开发了一个新的在线凸优化框架，解决了严格容量限制下延迟反馈的挑战。所提出的方法引入了一个半先知模型，并将问题新颖地归约为一个“延迟加权”的OCO问题。该方法为具有一阶和赌博机反馈的容量受限OCO建立了首个遗憾保证，表明对数容量足以接近标准速率。 AI
影响 在在线学习算法方面引入了理论进展，可能影响未来的AI系统设计。
排序理由 这是一篇发表在arXiv上的研究论文，详细介绍了一种针对特定类型优化问题的新理论框架。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.11711v1 Announce Type: cross Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, tracking resources are finite, and feedback from untracked rounds is permanently lost…
Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, tracking resources are finite, and feedback from untracked rounds is permanently lost. In this paper, we study delayed online convex op…