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  • title: Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
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            Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
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            Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

            Nov 28, 2022

            Speakers

            NK

            Nikita Kotelevskii

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            AA

            Aleksandr Artemenkov

            Speaker · 0 followers

            KF

            Kirill Fedyanin

            Speaker · 0 followers

            About

            This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions. First, we show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. Importantly, the approach allows to disentangle explicitly aleatoric and epistemic uncertainties. The resulting method works directly in the feature space. However, one can apply it to any neural net…

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            NeurIPS 2022

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