arXiv Machine Learning June 9, 2026 By Yicheng Li, Weiye Gan, Zuoqiang Shi, Qian Lin

Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay

Read the original on arXiv Machine Learning →

arXiv:2401. 01599v4 Announce Type: replace Abstract: The generalization error curve of certain kernel regression method aims at determining the exact order of generalization error with various source condition, noise level and choice of the regularization parameter rather than the minimax rate.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 8

Generalization in Deep Neural Networks: Minimax Rates for Gradient Methods

arXiv:2606. 06772v1 Announce Type: cross Abstract: Understanding the generalization performance of over-parameterized neural networks has become a central topic in deep learning theory.

By Junyu Zhou, Puyu Wang, Yunwen Lei, Marius Kloft, Yiming Ying

arXiv Machine Learning

Jul 30

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent

arXiv:2606. 06772v2 Announce Type: replace-cross Abstract: Characterizing the optimization dynamics and statistical performance of over-parameterized deep neural networks (DNNs) remains a central challenge in understanding the remarkable success of deep learning.

By Junyu Zhou, Puyu Wang, Dennis Wagner, Yunwen Lei, Marius Kloft, Yiming Ying

arXiv Machine Learning

Jun 8

Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization

arXiv:2505. 21423v3 Announce Type: replace Abstract: The remarkable generalization properties of overparameterized networks are often attributed to implicit biases, such as norm minimization at small learning rates and low sharpness in the Edge-of-Stability regime.

By Maria Matveev, Vit Fojtik, Hung-Hsu Chou, Gitta Kutyniok, Johannes Maly

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arXiv AI

Jun 29

How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks

arXiv:2606. 28242v1 Announce Type: cross Abstract: Understanding how performance scales jointly with model size and data is a central problem in modern machine learning.

By Julius Girardin, Emanuele Troiani, Yizhou Xu, Vittorio Erba, Florent Krzakala, Lenka Zdeborov\'a

arXiv Machine Learning

Jul 21

Spectral-Transport Stability and Benign Overfitting in Interpolating Learning

arXiv:2604. 08625v2 Announce Type: replace-cross Abstract: We develop a theoretical framework for generalization in the interpolating regime of statistical learning.

By Gustav Olaf Yunus Laitinen-Lundstr\"om Fredriksson-Imanov

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arXiv Machine Learning

Jul 16

How the Hessian-Spectrum of Neural Networks Depends on Data

arXiv:2607. 13631v1 Announce Type: new Abstract: The Hessian matrix is an important quantity of interest when it comes to studying the loss landscape and optimization dynamics in deep learning, as well as designing measures of generalization, second-order learning algorithms, etc.

By Jasraj Singh, Enea Monzio Compagnoni, Antonio Orvieto

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Generalization in Deep Neural Networks: Minimax Rates for Gradient Methods

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent

Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization

How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks

Spectral-Transport Stability and Benign Overfitting in Interpolating Learning

How the Hessian-Spectrum of Neural Networks Depends on Data