关于无限记忆在线预测的新理论

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

本文为具有无限记忆的在线预测场景引入了一个新的理论框架，特别针对由外部来源驱动的二元标记预测。该研究为可求和包络建立了精确的极小极大遗憾界限，为指数和多项式包络提供了精确的尺度。一个关键发现是，仅保留最近的输入可能导致遗憾值在多项式尺度上存在差异，并提出了一种在线牛顿预测器以达到上限。 AI

影响为在线预测算法提供了理论基础，可能影响未来人工智能模型的发展。

排序理由这是一篇发表在arXiv上的理论计算机科学论文。[lever_c_demoted from research: ic=1 ai=0.7]

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

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Signal score: 18 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 这是一篇发表在arXiv上的理论计算机科学论文。[lever_c_demoted from research: ic=1 ai=0.7]
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arXiv cs.LG TIER_1 English(EN) · Vaneet Aggarwal · 2026-08-28 04:00

无限记忆逻辑回归预测的尖锐最小最大遗憾

arXiv:2608.26515v1 Announce Type: cross Abstract: We study online prediction for a specific finite-alphabet, exogenously driven source with infinite input memory. Independent Rademacher inputs $(U_t)$ are observed sequentially, and the next binary mark has logit $\sum_{j=1}^{t}\t…