Action-conditional conformal prediction sets provide per-action safety guarantees for risk-averse policies that optimize conditional value-at-risk through pinball-loss minimization.
Batch multivalid conformal prediction.arXiv preprint arXiv:2209.15145
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
roles
method 1polarities
use method 1representative citing papers
ELCP integrates auxiliary data with a density-ratio-weighted kernel to enhance localized conformal prediction sets, maintaining marginal coverage and improving asymptotic local coverage.
A fair conformal classification method guarantees conditional coverage on adaptively identified subgroups defined via learned representations.
A step-function score transformation I_w = w·1{s≥w} reduces the estimated-coverage gap in Backward Conformal Prediction from about 4.2% to 1.1% on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
Pith review generated a malformed one-line summary.
Unifies MCBoost variants and proves convergence to Bregman projection with rates, finite-sample bounds, and covariate-shift transferability.
citing papers explorer
-
Conformal Risk-Averse Decision Making with Action Conditional Guarantee
Action-conditional conformal prediction sets provide per-action safety guarantees for risk-averse policies that optimize conditional value-at-risk through pinball-loss minimization.
-
Enhanced localized conformal prediction with imperfect auxiliary information
ELCP integrates auxiliary data with a density-ratio-weighted kernel to enhance localized conformal prediction sets, maintaining marginal coverage and improving asymptotic local coverage.
-
Fair Conformal Classification via Learning Representation-Based Groups
A fair conformal classification method guarantees conditional coverage on adaptively identified subgroups defined via learned representations.
-
Improving Backward Conformal Prediction via Non-Conformity Score Transformation
A step-function score transformation I_w = w·1{s≥w} reduces the estimated-coverage gap in Backward Conformal Prediction from about 4.2% to 1.1% on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
-
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Pith review generated a malformed one-line summary.
-
Multicalibration Boosting: Theory, Convergence, and Transferability
Unifies MCBoost variants and proves convergence to Bregman projection with rates, finite-sample bounds, and covariate-shift transferability.