Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Document Type
Conference Proceeding
Publication Title
Advances in Neural Information Processing Systems
Abstract
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 proposed 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 network by considering an embedding of the data induced by the network. We demonstrate the strong performance of the method in uncertainty estimation tasks on text classification problems and a variety of real-world image datasets, such as MNIST, SVHN, CIFAR-100 and several versions of ImageNet.
Publication Date
12-2022
Recommended Citation
N. Kotelevskii, et al, "Nonparametric Uncertainty Quantification for Single Deterministic Neural Network", in Advances in Neural Information Processing Systems,vol. 35, Dec 2022. https://proceedings.neurips.cc/paper_files/paper/2022/file/eb7389b039655fc5c53b11d4a6fa11bc-Paper-Conference.pdf
Additional Links
NeurIPS 2022 link: https://proceedings.neurips.cc/paper_files/paper/2022/hash/eb7389b039655fc5c53b11d4a6fa11bc-Abstract-Conference.html
Comments
IR conditions: non-described
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