Rectifying Conformity Scores for Better Conditional Coverage
We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage. The transformation is based on an estimate of the conditional quantile of conformity scores. The resulting method is particularly beneficial for constructing adaptive confidence sets in multi-output problems where standard conformal quantile regression approaches have limited applicability. We develop a theoretical bound that captures the influence of the accuracy of the quantile estimate on the approximate conditional validity, unlike classical bounds for conformal prediction methods that only offer marginal coverage. We experimentally show that our method is highly adaptive to the local data structure and outperforms existing methods in terms of conditional coverage, improving the reliability of statistical inference in various applications.
Code (0)
등록된 구현이 없습니다.
Tasks
Conformal Predictionquantile regressionSimilar Papers 제목 키워드 기반
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
Conformal prediction (CP) is a powerful framework for uncertainty quantification, providing prediction sets with coverage guarantees when calibrated on sufficient labeled data. However, in real-world applications where l…
Conformal PredictionPredictionPseudo LabelUncertainty QuantificationMultivariate Standardized Residuals for Conformal Prediction
While split conformal prediction guarantees marginal coverage, approaching the stronger property of conditional coverage is essential for reliable uncertainty quantification. Naive conformal scores, however, suffer from …
Adaptive Conformal Prediction by Reweighting Nonconformity Score
Despite attractive theoretical guarantees and practical successes, Predictive Interval (PI) given by Conformal Prediction (CP) may not reflect the uncertainty of a given model. This limitation arises from CP methods usin…
Conformal PredictionPredictionquantile regressionRegression Trees for Fast and Adaptive Prediction Intervals
Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create statistically valid prediction regions a…
PredictionPrediction IntervalsregressionvalidDistribution-informed Efficient Conformal Prediction for Full Ranking
Quantifying uncertainty is critical for the safe deployment of ranking models in real-world applications. Recent work offers a rigorous solution using conformal prediction in a full ranking scenario, which aims to constr…