mlsanityData to 7 Oct 2026

Generalising realisability in statistical learning theory under epistemic uncertainty

arXiv 2402.14759 ↗First posted 22 Feb 2024cs.LG cs.AI math.STcited by 5

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Abstract (arXiv)

The purpose of this paper is to look into how central notions in statistical learning theory, such as realisability, generalise under the assumption that train and test distribution are issued from the same credal set, i.e., a convex set of probability distributions. This can be considered as a first step towards a more general treatment of statistical learning under epistemic uncertainty.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

17 of 40 references.

References · 17

  1. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  2. 2021Domain Generalization: A Surveycited by 760
  3. 2020Out-of-Distribution Generalization via Risk Extrapolation (REx)cited by 737
  4. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  5. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 396
  6. 2020A Brief Review of Domain Adaptationcited by 287
  7. 2021Out of Distribution Generalization in Machine Learningcited by 59
  8. 2023Credal Bayesian Deep Learningcited by 45
  9. 2023Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?cited by 40
  10. 2023Second-Order Uncertainty Quantification: A Distance-Based Approachcited by 35
  11. 2024Credal Learning Theorycited by 30
  12. 2024CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Taskscited by 26
5 more
  1. 2023Random-Set Neural Networks (RS-NN)cited by 19
  2. 2022Epistemic Deep Learningcited by 15
  3. 2018Visions of a generalized probability theorycited by 13
  4. 2015An extension of McDiarmid's inequalitycited by 8
  5. 2023IBCL: Zero-shot Model Generation under Stability-Plasticity Trade-offscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Statistical inference with belief functions: A surveycited by 2
  2. 2026Credal Large Language Models for Semantic Commitment under Uncertaintycited by 2
  3. 2026A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classificationcited by 1
  4. 2026Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decompositioncited by 0
  5. 2026XU-RS: Explaining Credal Width in Random-Set Language Modelscited by 0