mlsanityData to 14 Oct 2026

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

arXiv 2110.13511 ↗First posted 26 Oct 2021cs.LGcited by 31

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Abstract (arXiv)

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. 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.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

16 of 29 references.

References · 16

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  4. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  5. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 909
  6. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  7. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  8. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  9. 2020Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learningcited by 210
  10. 2020Hyperparameter Ensembles for Robustness and Uncertainty Quantificationcited by 150
  11. 2016Learning in Implicit Generative Modelscited by 125
  12. 2018High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approachcited by 117
4 more
  1. 2021On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertaintycited by 87
  2. 2020Neural Ensemble Search for Uncertainty Estimation and Dataset Shiftcited by 56
  3. 2019Multivariate Uncertainty in Deep Learningcited by 27
  4. 2020AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Datacited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 12; 2024: 10; 2025: 3; 2026: 1.

31 papers.

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  1. 2023Credal Bayesian Deep Learningcited by 45
  2. 2023Optimal Training of Mean Variance Estimation Neural Networkscited by 18
  3. 2024Revisiting Essential and Nonessential Settings of Evidential Deep Learningcited by 15
  4. 2023Optimizing Distributed Training on Frontier for Large Language Modelscited by 12
  5. 2022Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecastingcited by 10
  6. 2024A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learningcited by 10
  7. 2023A Benchmark on Uncertainty Quantification for Deep Learning Prognosticscited by 9
  8. 2022Interpretable Uncertainty Quantification in AI for HEPcited by 8
  9. 2023Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Controlcited by 8
  10. 2023Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Searchcited by 8
  11. 2024Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNNcited by 8
  12. 2025Credal and Interval Deep Evidential Classificationscited by 8
19 more
  1. 2022Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimizationcited by 6
  2. 2022Sequential Bayesian Neural Subnetwork Ensemblescited by 5
  3. 2023Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensemblescited by 5
  4. 2023Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systemscited by 4
  5. 2024LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fieldscited by 4
  6. 2023Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Taskcited by 3
  7. 2023Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learningcited by 3
  8. 2024Employing Artificial Intelligence to Steer Exascale Workflows with Colmenacited by 3
  9. 2025Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)cited by 3
  10. 2024Deep Evidential Learning for Radiotherapy Dose Predictioncited by 2
  11. 2024An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Unitscited by 2
  12. 2024Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantificationcited by 2
  13. 2024DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methodscited by 2
  14. 2022Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustnesscited by 1
  15. 2023Toward Robust Uncertainty Estimation with Random Activation Functionscited by 1
  16. 2023A Neural Emulator for Uncertainty Estimation of Fire Propagationcited by 1
  17. 2025Uncertainty Quantification in Graph Neural Networks with Shallow Ensemblescited by 1
  18. 2024Instance-wise Uncertainty for Class Imbalance in Semantic Segmentationcited by 0
  19. 2026Uncertainty quantification for trustworthy deep learning: Methods and measurescited by 0