Online Conformal Prediction with Decaying Step Sizes
Abstract
We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences. However, unlike previous methods, we can simultaneously estimate a population quantile when it exists. Our theory and experiments indicate substantially improved practical properties: in particular, when the distribution is stable, the coverage is close to the desired level for every time point, not just on average over the observed sequence.
Cite
Text
Angelopoulos et al. "Online Conformal Prediction with Decaying Step Sizes." International Conference on Machine Learning, 2024.Markdown
[Angelopoulos et al. "Online Conformal Prediction with Decaying Step Sizes." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/angelopoulos2024icml-online/)BibTeX
@inproceedings{angelopoulos2024icml-online,
title = {{Online Conformal Prediction with Decaying Step Sizes}},
author = {Angelopoulos, Anastasios Nikolas and Barber, Rina and Bates, Stephen},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {1616-1630},
volume = {235},
url = {https://mlanthology.org/icml/2024/angelopoulos2024icml-online/}
}