Online conformal prediction with decaying step sizes
This work addresses the need for more reliable uncertainty quantification in online learning settings, offering incremental improvements over existing methods.
The paper tackles the problem of online conformal prediction by introducing a method with decaying step sizes, which improves coverage to be close to the desired level at every time point under stable distributions, rather than just on average.
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.