Summary of Sample Compression Unleashed: New Generalization Bounds For Real Valued Losses, by Mathieu Bazinet et al.
Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses
by Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed sample compression theory offers generalization guarantees for models that can be fully defined using a subset of the training data and a short message string. Unlike previous works, which focused on zero-one loss, this paper presents a framework for deriving bounds that hold for real-valued unbounded losses. The Pick-To-Learn (P2L) meta-algorithm is used to transform any machine-learning predictor into sample-compressed predictors, showcasing the tightness and versatility of the bounds through experiments with random forests and neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new theory about how to make predictions without needing all the data. It’s like finding a secret code that lets you understand what’s important in the data. This code is called “sample compression” and it helps us know when our predictions will be good or not. The authors use this idea with different types of machine learning models, like neural networks, to show how well their theory works. |
Keywords
» Artificial intelligence » Generalization » Machine learning
