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Anastasios and Stephen wrote a hands-on introduction to conformal prediction and distribution-free uncertainty quantification aimed at a reader interested in their practical implementation, who is not necessarily a statistician. There are many explanatory illustrations, accompanying YouTube tutorials, Python/PyTorch code samples, and examples. Any feedback is much appreciated and can be sent directly to the authors.
This video accompanying the first two sections of the above manuscript.
This video accompanying the third section of the above manuscript.
An introduction to risk-controlling for prediction tasks -- an extension of conformal prediction to handle many supervised learning tasks.
A comprehensive, statistical tutorial to conformal prediction and distribution-free calibration.
See Awesome Conformal Prediction for a comprehensive list of resources!
See Awesome Conformal Prediction for a comprehensive list of resources!
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