Cross-regularization: Adaptive Model Complexity through Validation Gradients
This addresses the need for automated regularization tuning in machine learning, offering a method that integrates with data augmentation and uncertainty calibration while maintaining efficiency, though it appears incremental as it builds on gradient-based optimization.
The paper tackles the problem of manual tuning for model regularization by introducing cross-regularization, which adapts regularization parameters using validation gradients during training, converging to cross-validation optima and revealing high noise tolerance and architecture-specific regularization in neural networks.
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.