English(EN) Parameter-Free Interval-Dynamic Regret under Heavy-Tailed Noise

新算法处理在线凸优化中的重尾噪声

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了一种新的在线凸优化方法，该方法可以在不知道噪声特性的先验知识的情况下处理重尾噪声。所提出的算法实现了与噪声特性自适应的遗憾界限，在噪声分布未知的场景中优于现有方法。这一进展对于数据质量可变且不可预测的应用（例如实时决策系统）具有重要意义。 AI

影响这项研究可以提高在嘈杂或不可预测环境中运行的AI系统的鲁棒性。

排序理由该条目是一篇学术论文，详细介绍了一种新的在线凸优化算法。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该条目是一篇学术论文，详细介绍了一种新的在线凸优化算法。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Vaneet Aggarwal · 2026-10-05 04:00

重尾噪声下的无参数区间动态遗憾

arXiv:2610.02258v1 Announce Type: new Abstract: We study online convex optimization with one unbiased stochastic subgradient per round and an unknown finite conditional $p$th noise moment, $1<p\le2$. For every fixed interval $I$ of length $n$ and comparator path with $\Lambda_I=1…