Summary of An Information Theoretic Perspective on Conformal Prediction, by Alvaro H.c. Correia et al.
An Information Theoretic Perspective on Conformal Prediction
by Alvaro H.C. Correia, Fabio Valerio Massoli, Christos Louizos, Arash Behboodi
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Machine Learning (stat.ML)
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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 A machine learning framework called Conformal Prediction (CP) is used to estimate uncertainty in predictions. This framework constructs prediction sets that contain the true answer with a specified probability. The size of these sets represents the uncertainty, with larger sets indicating higher uncertainty. In this paper, information theory is applied to connect CP to other notions of uncertainty. Three upper bounds are proved for the intrinsic uncertainty using conditional entropy and information theoretical inequalities. This connection enables principled training objectives for machine learning models and incorporates side information into conformal prediction. Theoretical results are validated in centralized and federated learning settings, showing reduced inefficiency (average prediction set size) for popular CP methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict uncertainty in machine learning is being developed. It’s called Conformal Prediction, or CP. This method makes sure that the true answer will be included in a certain range with a specific degree of certainty. The size of this range shows how uncertain we are about our prediction. In this paper, scientists found ways to connect CP to other ideas about uncertainty using information theory. They also showed two useful applications: making machine learning models from scratch and adding extra information to predictions. This was tested in different settings and showed that it can make predictions more efficient. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Probability
