AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is competitive with approaches based on Bayesian neural networks while benefiting from better computational scalability. However, building ensembles of neural networks is a challenging task because, in addition to choosing the right neural architecture or hyperparameters for each member of the ensemble, there is an added cost of training each model. To address this issue, we propose AutoDEUQ, an automated approach for generating an ensemble of deep neural networks. Our approach leverages joint neural architecture and hyperparameter search to generate ensembles. We use the law of total variance to decompose the predictive variance of deep ensembles into aleatoric (data) and epistemic (model) uncertainties. We show that AutoDEUQ outperforms probabilistic backpropagation, Monte Carlo dropout, deep ensemble, distribution-free ensembles, and hyper ensemble methods on a number of regression benchmarks.

Original languageEnglish (US)
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781665490627
StatePublished - 2022
Event26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: Aug 21 2022Aug 25 2022

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference26th International Conference on Pattern Recognition, ICPR 2022

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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