English(EN) Block-Norm Geometries for Online Mirror Descent with Sparse Losses

新的块范数几何增强了在线镜像下降算法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

一篇新研究论文介绍了一系列随机块范数镜像映射，旨在提高在线镜像下降算法的性能，尤其是在处理稀疏损失梯度时。与标准的欧几里得和熵几何相比，这些新几何提供了在遗憾界限方面与维度相关的多项式改进。该研究还解决了在稀疏性未知时选择几何的问题，提出了一种 Hedge 元算法，该算法在事后可以与最佳镜像映射竞争。 AI

影响引入了可能导致更有效的机器学习优化算法的新颖几何方法。

排序理由研究论文发表在 arXiv 上，详细介绍了优化算法的新数学技术。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Swati Gupta, Jai Moondra, Mohit Singh · 2026-09-14 04:00

面向稀疏损失的在线镜像下降的块范数几何

arXiv:2602.13177v2 Announce Type: replace-cross Abstract: The performance of online mirror descent depends critically on the geometry induced by its mirror map, yet standard algorithms largely rely on two canonical choices: Euclidean and entropic geometry. We show that these two …

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面向稀疏损失的在线镜像下降的块范数几何

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