Computationally efficient versions of conformal predictive distributions. / Vovk, Vladimir; Petej, Ivan; Nouretdinov, Ilia; Manokhin, Valery; Gammerman, Alex.

In: Neurocomputing, 09.10.2019.

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@article{f14db9d62e6d4faa9971a3636c26b85e,
title = "Computationally efficient versions of conformal predictive distributions",
abstract = "Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conformal predictive systems inherit the relative computational inefficiency of conformal predictors. In this paper we discuss two computationally efficient versions of conformal predictive systems, which we call split conformal predictive systems and cross-conformal predictive systems. The main advantage of split conformal predictive systems is their guaranteed validity, whereas for cross-conformal predictive systems validity only holds empirically and in the absence of excessive randomization. The main advantage of cross-conformal predictive systems is their greater predictive efficiency.",
keywords = "conformal prediction, cross-conformal prediction, inductive conformal prediction, predictive distributions, split conformal prediction, regression",
author = "Vladimir Vovk and Ivan Petej and Ilia Nouretdinov and Valery Manokhin and Alex Gammerman",
year = "2019",
month = "10",
day = "9",
language = "English",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Computationally efficient versions of conformal predictive distributions

AU - Vovk, Vladimir

AU - Petej, Ivan

AU - Nouretdinov, Ilia

AU - Manokhin, Valery

AU - Gammerman, Alex

PY - 2019/10/9

Y1 - 2019/10/9

N2 - Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conformal predictive systems inherit the relative computational inefficiency of conformal predictors. In this paper we discuss two computationally efficient versions of conformal predictive systems, which we call split conformal predictive systems and cross-conformal predictive systems. The main advantage of split conformal predictive systems is their guaranteed validity, whereas for cross-conformal predictive systems validity only holds empirically and in the absence of excessive randomization. The main advantage of cross-conformal predictive systems is their greater predictive efficiency.

AB - Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conformal predictive systems inherit the relative computational inefficiency of conformal predictors. In this paper we discuss two computationally efficient versions of conformal predictive systems, which we call split conformal predictive systems and cross-conformal predictive systems. The main advantage of split conformal predictive systems is their guaranteed validity, whereas for cross-conformal predictive systems validity only holds empirically and in the absence of excessive randomization. The main advantage of cross-conformal predictive systems is their greater predictive efficiency.

KW - conformal prediction

KW - cross-conformal prediction

KW - inductive conformal prediction

KW - predictive distributions

KW - split conformal prediction

KW - regression

M3 - Article

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -