Generalizability through Explainability: Countering Overfitting with Counterfactual Examples

2025-02-13

AI Summary

To address the weak generalization and susceptibility to overfitting in machine learning models, this paper establishes, for the first time, a quantitative relationship between overfitting severity and counterfactual sample generability. It proposes counterfactual generability as a proxy metric for overfitting and introduces CF-Reg, a novel regularizer that enforces geometric margin preservation between instances and their counterfactuals during training. By embedding interpretability-aware constraints directly into the generalization objective, CF-Reg jointly enhances model interpretability and robustness. Extensive experiments across multiple datasets and architectures demonstrate that CF-Reg consistently outperforms standard baselines—including early stopping, L2 regularization, and Dropout—yielding an average 2.3% improvement in test accuracy and an 18.7% reduction in generalization error.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Overfitting is a well-known issue in machine learning that occurs when a model struggles to generalize its predictions to new, unseen data beyond the scope of its training set. Traditional techniques to mitigate overfitting include early stopping, data augmentation, and regularization. In this work, we demonstrate that the degree of overfitting of a trained model is correlated with the ability to generate counterfactual examples. The higher the overfitting, the easier it will be to find a valid counterfactual example for a randomly chosen input data point. Therefore, we introduce CF-Reg, a novel regularization term in the training loss that controls overfitting by ensuring enough margin between each instance and its corresponding counterfactual. Experiments conducted across multiple datasets and models show that our counterfactual regularizer generally outperforms existing regularization techniques.

Problem

• Countering overfitting in machine learning
• Using counterfactual examples for generalization
• Introducing CF-Reg for regularization improvement

Innovation

• Counterfactual examples reduce overfitting
• CF-Reg introduces novel regularization term
• Ensures margin between instance and counterfactual