mlsanityData to 7 Oct 2026

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

arXiv 2501.03282 ↗First posted 5 Jan 2025cs.AI cs.LGcited by 17

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Abstract (arXiv)

Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2025.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 8.

17 papers.

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  1. 2025Uncertainty Quantification of Large Language Models through Multi-Dimensional Responsescited by 17
  2. 2025Epistemic Uncertainty in Conformal Scores: A Unified Approachcited by 9
  3. 2025Symmetric Aggregation of Conformity Scores for Efficient Uncertainty Setscited by 4
  4. 2025HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Modelcited by 3
  5. 2026Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMscited by 3
  6. 2025Multivariate Uncertainty Quantification with Tomographic Quantile Forestscited by 1
  7. 2026Managing Uncertainty in LLM-based Multi-Agent System Operationcited by 1
  8. 2026Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rulescited by 1
  9. 2026The Challenge of Out-Of-Distribution Detection in Motor Imagery BCIscited by 1
  10. 2026A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classificationcited by 1
  11. 2025LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representationscited by 0
  12. 2025Safe Uncertainty-Aware Learning of Robotic Suturingcited by 0
5 more
  1. 2025Localising Dropout Variance in Twin Networkscited by 0
  2. 2025Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insightscited by 0
  3. 2026Conditional Uncertainty-Aware Political Deepfake Detection with Stochastic Convolutional Neural Networkscited by 0
  4. 2026FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Driftcited by 0
  5. 2026Graph-Based Modeling of Financial Volatility Dynamicscited by 0