Conformal Prediction via Regression-as-Classification
<|reference_start|>Conformal Prediction via Regression-as-Classification: Conformal prediction (CP) for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed by estimating a distribution over the output, but in reality, such approaches can be sensitive to estimation error and yield unstable intervals.~Here, we circumvent the challenges by converting regression to a classification problem and then use CP for classification to obtain CP sets for regression.~To preserve the ordering of the continuous-output space, we design a new loss function and make necessary modifications to the CP classification techniques.~Empirical results on many benchmarks shows that this simple approach gives surprisingly good results on many practical problems.<|reference_end|>
@article{guha2024conformal,
    title={Conformal Prediction via Regression-as-Classification},
    author={Etash Guha, Shlok Natarajan, Thomas M"ollenhoff, Mohammad Emtiyaz
  Khan, Eugene Ndiaye},
    journal={International Conference of Learning Representations 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08168},
    primaryClass={cs.LG stat.ML}
}