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How Synthetic Labels Improve Conformal Prediction: A Perspective on Conditional Coverage
Conformal prediction provides distribution-free finite-sample marginal coverage, but post-hoc calibration data may be too scarce to learn how uncertainty varies across inputs. Meanwhile, abundant covariates can often be labeled cheaply by domain models or general-purpose language models. We study wh
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