Reproduce “Muddling Labels for Regularization, a novel approach to generalization” on OpenResearch

About this paper

Karim Lounici, Katia Meziani, Benjamin Riu

The "Muddling Labels for Regularization" (MLR) method integrates model training and hyperparameter calibration by introducing a novel risk criterion that uses permuted labels to prevent models from fitting spurious patterns. This approach achieves generalization performance comparable to or exceeding cross-validation, significantly reducing computational overhead and reliance on extensive hyperparameter tuning for linear regression models.

Key insights

• Generalization can be reconceptualized as the ability to avoid finding patterns where none exist, which is directly addressed by penalizing model fit on muddled data.
• The MLR criterion serves as an effective, smooth, and steep proxy for true generalization performance, making it highly suitable for gradient-based optimization.
• MLR procedures demonstrate strong robustness to initial optimization parameters and the number of permutations, indicating low sensitivity to internal hyperparameters and simplifying deployment.

Summary by alphaXiv.