Author

Stephen Bates

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#machine learning Preprint Oct 2026

Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective

This work introduces a family of generalized information measures based on the size and coverage of conformal prediction sets and formally relate conformal prediction to classical information-theoretic quantities and justifies using set-size reduction as an information gain metric.

Kevin B. Zhang, Stephen Bates · 0 citations