Conformal Prediction

Conformal Prediction is a machine learning framework designed to provide valid confidence measures for individual predictions. This method quantifies the uncertainty in predictions without assuming a specific distribution of the data, thereby offering a principled way to assess them. This section includes papers that explore theoretical advancements, algorithm development, and the application value of Conformal Prediction across various fields.