Enhancing Stability in Model Distillation: A Generic Approach Using Central Limit Theorem-Based Testing
Practical Solutions and Value Highlights:
Model distillation creates interpretable machine learning models with a simpler “student” model replicating a complex “teacher” model’s predictions.
Stabilizing model distillation involves a generic method using the central limit theorem approach.
This method determines necessary sample sizes for consistent results across different pseudo-samples and ensures stability in model selection.
The approach focuses on stability and reproducibility in model selection, providing consistent explanations for the teacher model across various data samples.
Experiments show that stabilization improves model structure consistency, especially in feature selection, and increasing candidate models and sample size enhances stability.
Empirical results validate the method’s effectiveness and highlight the challenge of distinguishing complex models without extensive pseudo-data.
Future work includes refining theoretical analysis, addressing teacher model uncertainty, and exploring alternative multiple-testing procedures.
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