ICML 2024poster28 citations
Online conformal prediction with decaying step sizes
Anastasios Nikolas Angelopoulos, Rina Barber, Stephen Bates
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.
BibTeX
@inproceedings{
angelopoulos2024online,
title={Online conformal prediction with decaying step sizes},
author={Anastasios Nikolas Angelopoulos and Rina Barber and Stephen Bates},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=2XkRIijUKw}
}