Refinement as a Service: Algorithmic Predictor Refinement
Prediction aggregation aims to combine information from multiple predictors into a more informative one. We study this question in the setting of calibrated predictors, where each prediction must equal the conditional expectation of the quantity being predicted given the predictor's signal. Given several calibrated input predictors and the feature distribution, but not the underlying Bayes probabilities, we ask when one can construct refined calibrated predictors that preserve the information in the original predictors and cannot be further refined using the available information. We formulate calibrated predictors as signaling schemes and define refinement through feature-independent gar...
| Authors | Wei Tang, Hanrui Zhang |
|---|---|
| Primary category | cs.GT |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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