English(EN) Streaming algorithms for robust max-min diversification

新的流式算法增强了鲁棒的最大最小多样性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

本文介绍了一种新的鲁棒最大最小多样性流式算法，解决了Amagata (AAAI23)先前表述中的局限性。所提出的算法提供了一种确定性方法，保证精确的k个内点，并提供$(2+\varepsilon)$-近似解，即使没有异常值。它利用独立于流中总点数的内存，并在广泛的参数范围内实现与流大小无关的摊销更新时间。 AI

影响提高了数据多样性算法的效率和鲁棒性，可能影响下游机器学习应用。

排序理由学术论文，详细介绍了一种针对特定机器学习问题的新算法。[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 →

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arXiv cs.LG TIER_1 English(EN) · Andrea Pietracaprina, Geppino Pucci, Stefano Zanon · 2026-10-03 04:00

流式算法用于鲁棒的最大最小多样性

arXiv:2610.01456v1 Announce Type: new Abstract: Given a set of $n$ points $X$ in a metric space and an integer $k$, max-min diversification aims to select $k$ points of $X$ maximizing their minimum pairwise distance. This objective function is however highly vulnerable to noisy p…

本文如何被排名

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流式算法用于鲁棒的最大最小多样性

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