mlsanityData to 13 Oct 2026

Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey

arXiv 2209.03580 ↗First posted 8 Sep 2022cs.AIcited by 11

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Abstract (arXiv)

Machine learning methods are increasingly widely used in high-risk settings such as healthcare, transportation, and finance. In these settings, it is important that a model produces calibrated uncertainty to reflect its own confidence and avoid failures. In this paper we survey recent works on uncertainty quantification (UQ) for deep learning, in particular distribution-free Conformal Prediction method for its mathematical properties and wide applicability. We will cover the theoretical guarantees of conformal methods, introduce techniques that improve calibration and efficiency for UQ in the context of spatiotemporal data, and discuss the role of UQ in the context of safe decision making.

Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). Builds on 51 of its 92 references that are in the map. First posted on arXiv 8 Sep 2022.

Builds on

51 of 92 references.

References · 51

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38 more
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 5; 2025: 4; 2026: 1.

11 papers.

  1. 2024Conformal Prediction: A Data Perspectivecited by 25
  2. 2024Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Modelscited by 12
  3. 2024Uncertainty Quantification of Surrogate Models using Conformal Predictioncited by 11
  4. 2025Calibrated Physics-Informed Uncertainty Quantificationcited by 8
  5. 2022What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problemscited by 5
  6. 2024Uncertainty quantification in metric spacescited by 3
  7. 2024Valid Error Bars for Neural Weather Models using Conformal Predictioncited by 3
  8. 2025Topology-Aware Conformal Prediction for Stream Networkscited by 3
  9. 2025Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spacescited by 2
  10. 2025Towards Reliable and Holistic Visual In-Context Learning Prompt Selectioncited by 2
  11. 2026Beyond Accuracy: Reliability and Uncertainty Estimation in Convolutional Neural Networkscited by 0