研究人员开发了用于学习具有算子值核的算子的 SGD 算法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

研究人员开发了一种在统计逆问题中估计回归算子的新方法。该方法利用正则化随机梯度下降 (SGD) 和算子值核，为预测和估计误差提供了与维度无关的界限。该技术提供了接近最优的收敛速度和高概率估计，适用于结构化预测和参数化偏微分方程。 AI

影响在无限维设置中引入了一种具有高概率保证的新颖技术，有可能提高结构化预测任务的性能。

排序理由这是一篇详细介绍机器学习新统计方法的学术论文。

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arXiv stat.ML TIER_1 English(EN) · Jia-Qi Yang, Lei Shi · 2026-04-28 04:00

通过带正则化的随机梯度下降和算子值核学习算子

arXiv:2504.18184v4 Announce Type: replace Abstract: We consider a class of statistical inverse problems involving the estimation of a regression operator from a Polish space to a separable Hilbert space, where the target lies in a vector-valued reproducing kernel Hilbert space in…