新方法分析非线性最小二乘模型中的泛化能力

作者 PulseAugur 编辑部 · [3 个来源] · 2026-06-07 19:33

研究人员开发了一种新方法来理解非线性最小二乘模型如何泛化。他们的方法利用平均算法稳定性来推导局部最小化器的误差界限。这些界限与训练参数处梯度模型的几何形状相关，提供了依赖于学习到的几何形状而非仅仅参数数量的见解。 AI

影响为理解模型泛化提供了理论基础，可能为未来的模型开发提供信息。

排序理由在arXiv上发表的学术论文。

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arXiv stat.ML TIER_1 English(EN) · Ayub Kharel, Ilja Kuzborski, Patrick Rebeschini, Yasin Abbasi-Yadkori · 2026-06-09 04:00

通过学习的特征几何在非线性最小二乘法中实现泛化

arXiv:2606.08799v1 Announce Type: new Abstract: We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-dependent effective dimension that reflects the geom…
arXiv stat.ML TIER_1 English(EN) · Yasin Abbasi-Yadkori · 2026-06-07 19:33

通过学习的特征几何实现非线性最小二乘法的泛化

We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-dependent effective dimension that reflects the geometry of the gradient model at the trained parame…
arXiv stat.ML TIER_1 English(EN) · Yasin Abbasi-Yadkori · 2026-06-07 19:33

通过学习的特征几何在非线性最小二乘法中实现泛化

We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-dependent effective dimension that reflects the geometry of the gradient model at the trained parame…