Online conformal prediction with decaying step sizes
UC Berkeley · University of Chicago · University of California, Berkeley
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摘要
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.