mlsanityData to 7 Oct 2026

Reasoning with random sets: An agenda for the future

arXiv 2401.09435 ↗First posted 19 Dec 2023math.ST cs.AI stat.MLcited by 6

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Abstract (arXiv)

In this paper, we discuss a potential agenda for future work in the theory of random sets and belief functions, touching upon a number of focal issues: the development of a fully-fledged theory of statistical reasoning with random sets, including the generalisation of logistic regression and of the classical laws of probability; the further development of the geometric approach to uncertainty, to include general random sets, a wider range of uncertainty measures and alternative geometric representations; the application of this new theory to high-impact areas such as climate change, machine learning and statistical learning theory.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 210 references that are in the map. First posted on arXiv 19 Dec 2023.

Builds on

10 of 210 references.

References · 10

  1. 2017Selective Classification for Deep Neural Networkscited by 799
  2. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  3. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 609
  4. 2023Random-Set Neural Networks (RS-NN)cited by 19
  5. 2022Epistemic Deep Learningcited by 15
  6. 2018Visions of a generalized probability theorycited by 13
  7. 2023Distributionally Robust Statistical Verification with Imprecise Neural Networkscited by 12
  8. 2021Uncertainty measures: The big picturecited by 8
  9. 2013A New Approach to Updating Beliefscited by 4
  10. 2022An introduction to optimization under uncertainty -- A short surveycited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025Random-Set Large Language Modelscited by 7
  2. 2026Epistemic Generative Adversarial Networkscited by 4
  3. 2026Random-Set Graph Neural Networkscited by 3
  4. 2026Statistical inference with belief functions: A surveycited by 2
  5. 2026A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classificationcited by 1
  6. 2026XU-RS: Explaining Credal Width in Random-Set Language Modelscited by 0