Large-Scale Probabilistic Prediction With and Without Validity Guarantees

Prof. Vladimir Vovk
UK, Royal Holloway, University of London
The topic of this talk is a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration), is computationally efficient, and preserves predictive efficiency. The price to pay for perfect calibration is that these probabilistic predictors produce imprecise (in practice, almost precise
for large data sets) probabilities. When these imprecise probabilities are merged into precise probabilities, the resulting predictors, while losing the theoretical property of perfect calibration, consistently outperform the existing methods in empirical studies.