A parameter-free algorithm for group-conditional online conformal prediction that achieves optimal coverage guarantees without learning-rate tuning.
Online conformal prediction via universal portfolio algorithms.arXiv preprint arXiv:2602.03168
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
OLCP and OLCP-Hedge achieve long-run valid coverage in non-exchangeable online settings with narrower prediction sets by localizing conformal prediction to covariates and selecting bandwidth via online convex optimization.
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.
citing papers explorer
-
Parameter-Free and Group Conditional Online Conformal Prediction
A parameter-free algorithm for group-conditional online conformal prediction that achieves optimal coverage guarantees without learning-rate tuning.
-
Online Localized Conformal Prediction
OLCP and OLCP-Hedge achieve long-run valid coverage in non-exchangeable online settings with narrower prediction sets by localizing conformal prediction to covariates and selecting bandwidth via online convex optimization.
-
Theoretical Foundations of Conformal Prediction
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.