μ & σ

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

  • Anastasios N. Angelopoulos, Stephen Bates

@misc{angelopoulos-gentle,
  title={A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification},
  author={Angelopoulos, Anastasios Nikolas and Bates, Stephen},
  url={https://arxiv.org/abs/2107.07511},
  journal={arXiv:2107.07511},
  year={2021}
}
					

Summary

Stephen Bates and I have spent the last several weeks writing the first draft of a living document (pdf link above) meant to teach people conformal prediction and distribution-free uncertainty quantification. The document is meant to be a hands-on introduction for a reader interested in the practical implementation of distribution-free UQ, who is not necessarily a statistician. We included many explanatory illustrations, examples, and Python/PyTorch code samples. If this document is useful to you, please consider citing it (click the quotation icon above for BibTex), and if you have any feedback, send it to my email!