Summary of Tighter Generalisation Bounds Via Interpolation, by Paul Viallard et al.
Tighter Generalisation Bounds via Interpolation
by Paul Viallard, Maxime Haddouche, Umut Şimşekli, Benjamin Guedj
First submitted to arxiv on: 7 Feb 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 presents a novel approach for deriving new PAC-Bayes generalization bounds using the (f, Γ)-divergence, allowing for interpolation between various probability divergences including KL, Wasserstein, and total variation. The authors explore the tightness of these bounds and connect them to earlier results in statistical learning, which are specific cases. Additionally, they instantiate their bounds as training objectives, providing non-trivial guarantees and practical performances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating new rules for measuring how well a machine learning model generalizes to new data. It’s important because it helps us understand when models will work well in real-life situations. The authors came up with a way to combine different ways of measuring how close two probability distributions are, which can help make our models better. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Probability
