Bayes-Sufficient Representations in Supervised Learning

This work defines Bayes-sufficient representations for supervised learning, focusing on information relevant for prediction based on a fixed decision problem and loss function. It introduces the concept of a Bayes quotient and connects the framework to property elicitation, showing how different loss functions require specific Bayes-optimal actions.