三篇新研究论文探讨了机器学习中的“领悟”（grokking）概念，特别是在岭回归的背景下。其中一篇论文提出了一种寻找最优正则化强度的数值程序，展示了接近最优的泛化能力。另一篇论文为使用梯度下降和权重衰减训练的线性模型中的领悟现象提供了理论证明，认为这是一种训练条件而非根本性缺陷。第三篇论文将物理学中的随机重置与岭回归联系起来，展示了重置到原点如何复制岭估计量，并探索了具有不同更新规律的替代谱滤波器。 AI
影响 这些论文为泛化和训练动力学提供了理论见解，可能为开发更强大的机器学习模型提供信息。
排序理由 该集群包含多篇关于理论机器学习主题的学术论文。
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We consider $L^2$-regularized linear (ridge) regression over a finite data sample $X$ with bounded covariance and linear prediction targets $y$ with additive isotropic noise of finite variance. We present an iterative procedure to compute the optimal regularization strength numer…
arXiv:2601.19791v3 Announce Type: replace-cross Abstract: We study grokking, the onset of generalization long after overfitting, in a classical ridge regression setting. We prove end-to-end grokking results for learning over-parameterized linear regression models using gradient d…
arXiv:2605.30059v1 Announce Type: cross Abstract: We connect stochastic resetting from non-equilibrium statistical physics with ridge regularization in statistical learning. For linear gradient flow, resetting to the origin at rate $r$ produces stationary mean $(X^\top X+rI)^{-1}…
We connect stochastic resetting from non-equilibrium statistical physics with ridge regularization in statistical learning. For linear gradient flow, resetting to the origin at rate $r$ produces stationary mean $(X^\top X+rI)^{-1}X^\top y$, exactly the ridge estimator with penalt…