English(EN) Learn-Then-Differentiate Gradient Estimation

新框架统一并分析 Learn-Then-Differentiate 梯度估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

已开发出一种新的 Learn-Then-Differentiate (LTD) 梯度估计框架，它统一了现有方法并提供了准确性保证。该框架解释了 LTD 区分了什么以及它如何准确地估计梯度，特别是对于具有加权表示的模型。研究表明，拟合模型的准确性保证可以转化为梯度准确性，在特定的平滑条件下接近标准的蒙特卡洛速率。这种统一的方法包括了核回归和核岭回归等成熟技术，同时也为多核学习和光滑神经网络提供了新的见解。 AI

影响为理解和改进机器学习模型中的梯度估计技术提供了理论基础。

排序理由该集群包含一篇学术论文，详细介绍了梯度估计方法的新理论框架和分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Nifei Lin, Qingkai Zhang, L. Jeff Hong · 2026-10-02 04:00

学习后区分梯度估计

arXiv:2609.38842v1 Announce Type: cross Abstract: Learn-then-differentiate (LTD) estimates gradients by fitting a model to simulation outputs and differentiating it. We develop a unified framework explaining what LTD differentiates and how accurately it estimates gradients. For m…

本文如何被排名

报道来源 [1]

学习后区分梯度估计

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