Summary of High-dimensional Analysis Of Knowledge Distillation: Weak-to-strong Generalization and Scaling Laws, by M. Emrullah Ildiz et al.
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws
by M. Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga, Marco Mondelli, Samet Oymak
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper provides a sharp characterization of knowledge distillation in ridgeless, high-dimensional regression under model shift and distribution shift settings. The authors establish non-asymptotic bounds for the target model’s risk in terms of sample size and data distribution, revealing the optimal surrogate model’s form. This leads to insights on weak-to-strong generalization, showing that W2S training can outperform strong-label training but cannot improve the data scaling law. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists use machine learning techniques to better understand how one model can help another learn from its mistakes. They look at a specific type of situation where a simpler model is used as a guide for training a more complex model. The researchers discover that in certain situations, using the simpler model’s output as labels can actually make the complex model perform better than if it was trained with perfect labels. However, they also find limitations to this approach. |
Keywords
» Artificial intelligence » Generalization » Knowledge distillation » Machine learning » Regression
