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New MRT-FD method optimizes stochastic bilevel problems

研究人员推出了一种新的一阶方法 MRT-FD,旨在解决随机双层优化问题。该方法同时管理上层变量、下层解以及源自隐式微分的辅助响应。MRT-FD 以 $\mathcal{O}(\varepsilon^{-4-2/p})$ 的随机梯度查询实现了 $\varepsilon$-平稳点,这与任何固定有限平滑阶数 $p$ 的证明预言机下界相匹配。这一进展有效地缩小了在这类优化问题中随机一阶预言机设置下的复杂度差距。 AI

影响 引入了一种新的优化方法,可以提高复杂 AI 模型训练的效率。

排序理由 该集群包含一篇详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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New MRT-FD method optimizes stochastic bilevel problems

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该集群包含一篇详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Linxuan Pan, Junchi Yang ·

    具有一阶预言机的最优随机双层优化

    arXiv:2610.01843v1 Announce Type: cross Abstract: We study nonconvex--strongly-convex bilevel optimization under a stochastic first-order oracle. We introduce MRT-FD, a single-loop first-order method that simultaneously tracks the upper-level variable, the lower-level solution, a…