Large-scale probabilistic predictors with and without guarantees of validity

@inproceedings{Vovk2015LargescalePP,
  title={Large-scale probabilistic predictors with and without guarantees of validity},
  author={Vladimir Vovk and Ivan Petej and Valentina Fedorova},
  booktitle={NIPS},
  year={2015}
}
This paper studies theoretically and empirically a method of turning machinelearning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally efficient. 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… CONTINUE READING
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Large-scale probabilistic predictors with and without guarantees of validity

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