mlsanityData to 14 Oct 2026

Regression Prior Networks

arXiv 2006.11590 ↗First posted 20 Jun 2020cs.LG stat.MLcited by 28

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Abstract (arXiv)

Prior Networks are a recently developed class of models which yield interpretable measures of uncertainty and have been shown to outperform state-of-the-art ensemble approaches on a range of tasks. They can also be used to distill an ensemble of models via Ensemble Distribution Distillation (EnD2), such that its accuracy, calibration and uncertainty estimates are retained within a single model. However, Prior Networks have so far been developed only for classification tasks. This work extends Prior Networks and EnD2 to regression tasks by considering the Normal-Wishart distribution. The properties of Regression Prior Networks are demonstrated on synthetic data, selected UCI datasets and a monocular depth estimation task, where they yield performance competitive with ensemble approaches.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Jun 2020.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 8; 2024: 5; 2025: 3; 2026: 1.

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  1. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 819
  2. 2023Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Modelscited by 150
  3. 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 104
  4. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 101
  5. 2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
  6. 2023On Second-Order Scoring Rules for Epistemic Uncertainty Quantificationcited by 38
  7. 2023Second-Order Uncertainty Quantification: A Distance-Based Approachcited by 35
  8. 2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?cited by 35
  9. 2022Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learningcited by 27
  10. 2022Interpretable Self-Aware Neural Networks for Robust Trajectory Predictioncited by 22
  11. 2021Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targetscited by 19
  12. 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?cited by 18
16 more
  1. 2024Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyondcited by 18
  2. 2021NOMU: Neural Optimization-based Model Uncertaintycited by 16
  3. 2022ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inferencecited by 15
  4. 2021Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributionscited by 13
  5. 2021Improving evidential deep learning via multi-task learningcited by 13
  6. 2024Uncertainty Regularized Evidential Regressioncited by 13
  7. 2022Disentangling Uncertainty in Machine Translation Evaluationcited by 9
  8. 2025An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regressioncited by 9
  9. 2023Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regressioncited by 7
  10. 2024RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shiftscited by 2
  11. 2025Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision Layercited by 2
  12. 2025Uncertainty Quantification for Regression: A Unified Framework based on kernel scorescited by 2
  13. 2023Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?cited by 1
  14. 2023Spatial Bayesian Neural Networkscited by 1
  15. 2023Continuous Time Evidential Distributions for Irregular Time Seriescited by 0
  16. 2026Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifiercited by 0