Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective
Provides rigorous mathematical framework for interpreting uncertainty in conformal prediction methods
AI Summary
Researchers establish theoretical foundations showing how conformal prediction set sizes quantify information gain, relating them to classical information-theoretic measures.
Excerpt
Conformal prediction is a popular tool for uncertainty quantification that outputs prediction sets with finite-sample coverage guarantees. While prediction set size is commonly used as a heuristic measure of uncertainty, the information-theoretic basis for this interpretation remains poorly understood. In this work, we provide such a foundation using a decision-theoretic generalization of entropy tailored to set-valued prediction. In particular, we introduce a family of generalized information m