H-Consistency Bounds for Machine Learning
Analysis
This paper introduces and analyzes H-consistency bounds, a novel approach to understanding the relationship between surrogate and target loss functions in machine learning. It provides stronger guarantees than existing methods like Bayes-consistency and H-calibration, offering a more informative perspective on model performance. The work is significant because it addresses a fundamental problem in machine learning: the discrepancy between the loss optimized during training and the actual task performance. The paper's comprehensive framework and explicit bounds for various surrogate losses, including those used in adversarial settings, are valuable contributions. The analysis of growth rates and minimizability gaps further aids in surrogate selection and understanding model behavior.
Key Takeaways
- •Introduces H-consistency bounds, a new framework for analyzing surrogate loss functions.
- •Provides tighter guarantees than Bayes-consistency and H-calibration.
- •Offers explicit bounds for various surrogate losses, including those used in adversarial settings.
- •Analyzes growth rates and minimizability gaps to guide surrogate selection.
“The paper establishes tight distribution-dependent and -independent bounds for binary classification and extends these bounds to multi-class classification, including adversarial scenarios.”