Marginally Useful: Formalizing the Information Gap in Conformal Prediction
The episode discusses Peter Cotton's paper, "Marginally Useful: Formalizing the Information Gap in Conformal Prediction." The hosts explain that conformal prediction provides a coverage guarantee but is limited because it cannot recover information lost due to input-dependent errors. The key finding is that this lost information is quantified by mutual information between the residual and the input.