The insights from this work on conformal prediction for regression can be applied to other areas of machine learning, such as uncertainty quantification and out-of-distribution detection. In uncertainty quantification, the techniques developed for estimating the distribution over the output space can be leveraged to provide more accurate uncertainty estimates for machine learning models. By incorporating conformal prediction methods, models can provide prediction intervals that reflect the uncertainty in the predictions, leading to more reliable and trustworthy results.
For out-of-distribution detection, the principles of conformal prediction can be used to determine whether a new data point belongs to the same distribution as the training data. By constructing prediction sets based on the conformity scores, models can identify instances that deviate significantly from the training data distribution, indicating potential out-of-distribution samples. This can help improve the robustness and reliability of machine learning models in detecting anomalies and outliers.