arXiv Machine Learning August 26, 2026 By Duy Hoang, Bastien Berret, Olivier Bruneau, Laurent Fribourg

A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm

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The paper proposes an analytic method for determining the optimal early‑stopping time in training neural networks, avoiding the need for gradient‑descent training. It uses Rademacher complexity with an L1‑norm to estimate generalization error, offering a more general approach than previous random‑matrix‑theory based methods. The framework is demonstrated on linear regression and extended to nonlinear neural networks via linear probing, as shown in a MNIST classification example.

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