Cross-regularization: Adaptive Model Complexity through Validation Gradients

• URL: http://arxiv.org/abs/2506.19755v1
• Date: Tue, 24 Jun 2025 16:15:50 GMT
• Title: Cross-regularization: Adaptive Model Complexity through Validation Gradients
• Authors: Carlos Stein Brito,
• Abstract summary: Cross-regularization resolves tradeoffs by adapting regularization parameters through validation gradients during training.<n>When implemented through noise injection in neural networks, this approach reveals striking patterns.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model regularization requires extensive manual tuning to balance complexity against overfitting. Cross-regularization resolves this tradeoff by directly adapting regularization parameters through validation gradients during training. The method splits parameter optimization - training data guides feature learning while validation data shapes complexity controls - converging provably to cross-validation optima. When implemented through noise injection in neural networks, this approach reveals striking patterns: unexpectedly high noise tolerance and architecture-specific regularization that emerges organically during training. Beyond complexity control, the framework integrates seamlessly with data augmentation, uncertainty calibration and growing datasets while maintaining single-run efficiency through a simple gradient-based approach.

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