Refinement as a Service: Algorithmic Predictor Refinement
The paper studies how to improve calibrated predictors by combining information from multiple predictors using only observable data, avoiding complex Bayes probabilities. It formalizes refinement as a signal simulation problem, showing that while refining deterministic predictors with two inputs is efficient, refining randomized ones is also possible in polynomial time.