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新研究探讨了带上限参与的Hessian相似优化

一篇题为“Hessian-Similar Optimization with Capped Participation: Lower Bounds and Virtual Masters”的新研究论文已在arXiv上发表。该论文介绍了在每轮只有部分客户端可以参与的情况下,新颖的优化技术。它为具有严格参与上限的算法设定了下界,并提出了一种使用Catalyst的虚拟主控方法,在二次问题上实现了改进的预期轮数。研究还包括一项比较所提出方法与现有算法的综合研究。 AI

影响 引入了分布式优化新的理论界限和实用方法,可能提高大规模机器学习的效率。

排序理由 该集群包含一篇发表在arXiv上的研究论文,详细介绍了新的优化技术。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新研究探讨了带上限参与的Hessian相似优化

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该集群包含一篇发表在arXiv上的研究论文,详细介绍了新的优化技术。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Rui Wang ·

    Hessian-相似优化与有界参与:下界与虚拟主控

    arXiv:2610.33835v1 Announce Type: cross Abstract: What is the round complexity of Hessian-similar optimization when each round can contact at most $s$ of $m$ clients? Prior rates permit a free master or full participation and do not characterize strict caps. We count every one-ve…