Pith. sign in

REVIEW 2 cited by

Online conformal prediction with decaying step sizes

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.01139 v2 pith:NDFUZJ4M submitted 2024-02-02 stat.ML cs.LGstat.ME

classification stat.MLcs.LGstat.ME
keywords conformalcoveragedecayingmethodsonlinepredictionprevioussizes
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Relevance-Aware Thresholding in Online Conformal Prediction for Time Series

    cs.LG 2025-10 conditional novelty 5.0 of 10

    Replacing the binary inside/outside error in PID and ECI online conformal prediction with smooth relevance functions can shrink prediction intervals while keeping long-run coverage on several time-series benchmarks.

  2. Dynamic Estimation Loss Control in Variational Quantum Sensing via Online Conformal Inference

    quant-ph 2025-05 conditional novelty 5.0 of 10

    A dynamic variational quantum sensing method using online conformal inference controls the long-term estimation loss at a user-specified level while updating circuit and estimator parameters.

Pith tools